{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis Code and Model Data for \"Rising temperatures erode human sleep globally\"\n",
    "### Authors: Kelton Minor, Andreas Bjerre-Nielsen, Sigga Svala Jonasdottir, Sune Lehmann, Nick Obradovich"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Install R Jupyter Notebooks via Conda before running notebook\n",
    "#Data structuring code has been commented out\n",
    "\n",
    "Access model data and plot figures by running uncommented code chunks in notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# #Install relevant packages\n",
    "# install.packages(\"bit64\")\n",
    "# install.packages(\"ggthemes\")\n",
    "# install.packages(\"sandwich\")\n",
    "# install.packages(\"lmtest\")\n",
    "# install.packages(\"ggplot2\")\n",
    "# install.packages(\"Hmisc\")\n",
    "# install.packages(\"RColorBrewer\")\n",
    "# install.packages(\"gplots\")\n",
    "# install.packages(\"lfe\")\n",
    "# install.packages(\"stargazer\")\n",
    "# install.packages(\"plyr\")\n",
    "# install.packages(\"dplyr\")\n",
    "# install.packages(\"data.table\")\n",
    "# install.packages(\"caTools\")\n",
    "# install.packages(\"stringr\")\n",
    "# install.packages(\"knitr\")\n",
    "# install.packages(\"pander\")\n",
    "# install.packages(\"GGally\")\n",
    "# install.packages(\"ggExtra\")\n",
    "# install.packages(\"gridExtra\")\n",
    "# install.packages(\"viridis\")\n",
    "# install.packages(\"devtools\")\n",
    "# install.packages(\"cowplot\")\n",
    "# install.packages(\"captioner\")\n",
    "# install.packages(\"weathermetrics\")\n",
    "# install.packages(\"devtools\")\n",
    "# devtools::install_github(\"wilkelab/cowplot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(bit64) # long integers\n",
    "library(ggthemes) #themes for plotting\n",
    "library(sandwich) #robust se estimator\n",
    "library(lmtest) #lm functions\n",
    "library(ggplot2) #nice plots\n",
    "library(Hmisc) #multipurpose package\n",
    "library(RColorBrewer) #make nice colors\n",
    "library(gplots) #nice plotting\n",
    "library(lfe) #run linear fixed effects models\n",
    "library(stargazer) #output nice tables\n",
    "library(plyr) #for additional manipulation functions\n",
    "library(dplyr) #for data frame manipulation functions\n",
    "library(data.table) #for data.table functions\n",
    "library(caTools) #functions for fast running mean and sd\n",
    "library(stringr) #string tools\n",
    "library(knitr) #knitting\n",
    "library(pander) #pandering\n",
    "library(ggExtra) # for marginal histograms\n",
    "library(gridExtra) # for multiplots\n",
    "library(viridis) # for colors\n",
    "library(gtable) # for gtable filter\n",
    "library(cowplot)\n",
    "library(captioner)\n",
    "library(weathermetrics)\n",
    "library(grid) #plotting package\n",
    "\n",
    "# set cores for parallel processing\n",
    "#registerDoMC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define and load R functions\n",
    "# functions for grabbing p-values and coefficients from summaries of felm models\n",
    "coef <- function(reg, name) {\n",
    "    if (round(reg$coefficients[rownames(reg$coefficients)==name, 1], 3) == 0) {\n",
    "        format(round(reg$coefficients[rownames(reg$coefficients)==name, 1], 4), scientific=FALSE) \n",
    "    }\n",
    "    else round(reg$coefficients[rownames(reg$coefficients)==name, 1], 3)\n",
    "}\n",
    "pval <- function(reg, name) {\n",
    "  if (round(reg$coefficients[rownames(reg$coefficients)==name, 4], 3) >=0.001) \n",
    "  {round(reg$coefficients[rownames(reg$coefficients)==name, 4], 3)} \n",
    "  else paste(\"< 0.001\")\n",
    "}\n",
    "\n",
    "# mode function\n",
    "getmode <- function(v) {\n",
    "   uniqv <- unique(v)\n",
    "   uniqv[which.max(tabulate(match(v, uniqv)))]\n",
    "}\n",
    "\n",
    "# captioner setting\n",
    "fig_num <- captioner()\n",
    "tab_num <- captioner(prefix=\"Table\")\n",
    "eq_num <- captioner(prefix=\"Equation\")\n",
    "sfig_num <- captioner(prefix=\"Figure S\", auto_space=FALSE)\n",
    "stab_num <- captioner(prefix=\"Table S\", auto_space=FALSE)\n",
    "seq_num <- captioner(prefix=\"Equation S\", auto_space=FALSE)\n",
    "\n",
    "#bin plot function\n",
    "source('binned_plot_function_rep.R')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #load climate and sleep dataframe\n",
    "# Climate_Controls_DF <- read.csv(\"climate_sleeplot.csv\", header=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Inclusion Criteria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Period of use (from first to last use)\n",
    "# #Include those with at least 28 nights with sleep entries\n",
    "# Climate_Controls_DF <- subset(Climate_Controls_DF, (count >= 28))\n",
    "\n",
    "# #Percent of days with use\n",
    "# #Include those who wore wristband at least 25% of days from first to last use\n",
    "# Climate_Controls_DF <- subset(Climate_Controls_DF, (percent_days_w_sleepentries >= .25))\n",
    "\n",
    "# #Robustness Checks/Sensitivity Analyses\n",
    "# #at least 56 nights with sleep entries\n",
    "# Climate_Controls_DF_56nights <- subset(Climate_Controls_DF, (count >= 56))\n",
    "\n",
    "# #at least 84 nights with sleep entries\n",
    "# Climate_Controls_DF_84nights <- subset(Climate_Controls_DF, (count >= 84))\n",
    "\n",
    "# #at least 112 nights with sleep entries\n",
    "# Climate_Controls_DF_112nights <- subset(Climate_Controls_DF, (count >= 112))\n",
    "\n",
    "# #Robustness Checks/Sensitivity Analyses\n",
    "# Wore wristband at least 50% of the time\n",
    "# Climate_Controls_DF_50percent <- subset(Climate_Controls_DF_Sub, (percent_days_w_sleepentries >= .50))\n",
    "\n",
    "# #Wore wristband at least 75% of the time\n",
    "# Climate_Controls_DF_75percent <- subset(Climate_Controls_DF, (percent_days_w_sleepentries >= .75))\n",
    "\n",
    "# #Wore wristband at least 85% of the time\n",
    "# Climate_Controls_DF_85percent <- subset(Climate_Controls_DF, (percent_days_w_sleepentries >= .85))\n",
    "\n",
    "# #Night sleep timing inclusion criteria\n",
    "# #wide night sleep timing inclusion criteria (data driven 3 SDs above and below mean)\n",
    "# Climate_Controls_DF$onset <- as.numeric(levels(Climate_Controls_DF$onset))[Climate_Controls_DF$onset]\n",
    "# Climate_Controls_DF <- subset(Climate_Controls_DF, (onset > 19 | onset < 8))\n",
    "# Climate_Controls_DF <- subset(Climate_Controls_DF, (offset < 15))\n",
    "\n",
    "# # #Robustness Checks/Sensitivity Analyses\n",
    "# # #1. narrow time filters (Walch, Altoff)\n",
    "# #Climate_Controls_DF_Walch <- subset(Climate_Controls_DF, (onset > 19 | onset < 3))\n",
    "# #Climate_Controls_DF_Walch <- subset(Climate_Controls_DF_Walch, (offset > 3 & offset < 11))\n",
    "\n",
    "# #2. no time filters (24 hr specification with no filters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ## Calculate heat index variables----\n",
    "\n",
    "# #convert to data.table\n",
    "# weather <- as.data.table(Climate_Controls_DF)\n",
    "# #create relative humidity copy variable for equation\n",
    "# weather[,humid := rhum_rean2]\n",
    "\n",
    "# ## from NOAA's heat index equation\n",
    "# ## http://www.wpc.ncep.noaa.gov/html/heatindex_equation.shtml\n",
    "# weather[, tmaxf := tmin*1.8 + 32] #convert C to F\n",
    "# weather[, nwsheatindex :=\n",
    "#          - 42.379\n",
    "#          + 2.04901523*tmaxf\n",
    "#          + 10.14333127*humid\n",
    "#          - .22475541*tmaxf*humid\n",
    "#          - .00683783*tmaxf*tmaxf\n",
    "#          - .05481717*humid*humid\n",
    "#          + .00122874*tmaxf*tmaxf*humid\n",
    "#          + .00085282*tmaxf*humid*humid\n",
    "#          - .00000199*tmaxf*tmaxf*humid*humid] # across all values\n",
    "# weather[humid < 13 & (tmaxf > 80 & tmaxf < 112), nwsheatindex :=\n",
    "# nwsheatindex - ((13-humid)/4)*sqrt((17-abs(tmaxf-95.))/17)] # adjustment 1\n",
    "# weather[humid > 85 & (tmaxf > 80 & tmaxf < 87), nwsheatindex :=\n",
    "# nwsheatindex +  ((humid-85)/10) * ((87-tmaxf)/5)] # adjustment 2\n",
    "# weather[tmaxf <= 80, nwsheatindex :=  0.5*(tmaxf + 61.0 +\n",
    "# ((tmaxf-68.0)*1.2) + (humid*0.094))] # adjustment for cooler temps\n",
    "# weather[, nwsheatindex := (nwsheatindex-32)/1.8] # convert to equivalent celsius units\n",
    "\n",
    "# #rename heat index\n",
    "# weather$heat.index <- weather$nwsheatindex\n",
    "\n",
    "# #convert DT to DF\n",
    "# setDF(weather)\n",
    "# Climate_Controls_DF <- weather\n",
    "# rm(weather)\n",
    "# gc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Structure vars for modeling\n",
    "\n",
    "# #create unique state-year-month of the study variable\n",
    "# cols <- c(\"state\", \"year\", \"month\")\n",
    "# Climate_Controls_DF$stateyrmn <- do.call(paste, c(Climate_Controls_DF[cols], sep=\"-\"))\n",
    "\n",
    "# #create unique country-year-month of the study variable\n",
    "# cols <- c(\"country\", \"year\", \"month\")\n",
    "# Climate_Controls_DF$countryyrmn <- do.call(paste, c(Climate_Controls_DF[cols], sep=\"-\"))\n",
    "\n",
    "# #create unique state-week of the study variable\n",
    "# cols <- c(\"state\", \"unique_week_no\")\n",
    "# Climate_Controls_DF$statewk <- do.call(paste, c(Climate_Controls_DF[cols], sep=\"-\"))\n",
    "\n",
    "# #create short sleep duration binary variables\n",
    "# Climate_Controls_DF$short_7hr_sleep[Climate_Controls_DF$sleep_duration_hour<7] <- 1\n",
    "# Climate_Controls_DF[c(\"short_7hr_sleep\")][is.na(Climate_Controls_DF[c(\"short_7hr_sleep\")])] <- 0\n",
    "\n",
    "# Climate_Controls_DF$short_6hr_sleep[Climate_Controls_DF$sleep_duration_hour<6] <- 1\n",
    "# Climate_Controls_DF[c(\"short_6hr_sleep\")][is.na(Climate_Controls_DF[c(\"short_6hr_sleep\")])] <- 0\n",
    "\n",
    "# Climate_Controls_DF$short_5hr_sleep[Climate_Controls_DF$sleep_duration_hour<5] <- 1\n",
    "# Climate_Controls_DF[c(\"short_5hr_sleep\")][is.na(Climate_Controls_DF[c(\"short_5hr_sleep\")])] <- 0\n",
    "\n",
    "# #create sleep duration minutes DV\n",
    "# Climate_Controls_DF$sleep_duration_minutes <- Climate_Controls_DF$sleep_duration_hour*60\n",
    "\n",
    "# #create sleep duration 24hr minutes DV\n",
    "# Climate_Controls_DF$sleep_duration_24hr_minutes <- Climate_Controls_DF$sleep_duration_hour_total*60\n",
    "\n",
    "# #create +24hr onset\n",
    "# index <- (Climate_Controls_DF$onset > 0) & (Climate_Controls_DF$onset < 8)\n",
    "# Climate_Controls_DF$onset[index] <- (Climate_Controls_DF$onset[index] + 24)\n",
    "# Climate_Controls_DF$onset <- Climate_Controls_DF$onset*60\n",
    "\n",
    "# #create +24hr midsleep\n",
    "# index <- (Climate_Controls_DF$midsleep > 0) & (Climate_Controls_DF$midsleep < 13)\n",
    "# Climate_Controls_DF$midsleep[index] <- (Climate_Controls_DF$midsleep[index] + 24)\n",
    "# Climate_Controls_DF$midsleep <- Climate_Controls_DF$midsleep*60\n",
    "\n",
    "# #create +24hr offset\n",
    "# index <- (Climate_Controls_DF$offset > 0) & (Climate_Controls_DF$offset < 15)\n",
    "# Climate_Controls_DF$offset[index] <- (Climate_Controls_DF$offset[index] + 24)\n",
    "# Climate_Controls_DF$offset <- Climate_Controls_DF$offset*60\n",
    "\n",
    "# #create binary awakening variable\n",
    "# Climate_Controls_DF$Awake[Climate_Controls_DF$countAwake>0] <- 1\n",
    "# Climate_Controls_DF[c(\"Awake\")][is.na(Climate_Controls_DF[c(\"Awake\")])] <- 0\n",
    "\n",
    "# #convert GHCND precipitation and precip norm into cm\n",
    "# Climate_Controls_DF$prcp <- Climate_Controls_DF$prcp/10\n",
    "# Climate_Controls_DF$prcp_norm_w7 <- Climate_Controls_DF$prcp_norm_w7/10\n",
    "\n",
    "# #convert REAN2 precipitation precip norm reanalysis from kg/(m^2/s) into cm \n",
    "# Climate_Controls_DF$prcp_rean2 <- Climate_Controls_DF$prcp_rean2*8640\n",
    "# Climate_Controls_DF$prcp_norm_7_rean2 <- Climate_Controls_DF$prcp_norm_7_rean2*8640\n",
    "\n",
    "# #convert all numeric values\n",
    "# Climate_Controls_DF$sleep_duration_hour = as.numeric(Climate_Controls_DF$sleep_duration_hour)\n",
    "# Climate_Controls_DF$sleep_duration_hour_total = as.numeric(Climate_Controls_DF$sleep_duration_hour_total)\n",
    "# Climate_Controls_DF$countAwake = as.numeric(Climate_Controls_DF$countAwake)\n",
    "# Climate_Controls_DF$GDP <- as.numeric(levels(Climate_Controls_DF$GDP))[Climate_Controls_DF$GDP]\n",
    "\n",
    "# #create binary napping variable\n",
    "# Climate_Controls_DF$nap_taken[Climate_Controls_DF$nap_duration_hour>0] <- 1\n",
    "# Climate_Controls_DF[c(\"nap_taken\")][is.na(Climate_Controls_DF[c(\"nap_taken\")])] <- 0\n",
    "\n",
    "# #convert all categorical variables into factors\n",
    "# Climate_Controls_DF$unique_day_no <- as.factor(Climate_Controls_DF$date)\n",
    "# Climate_Controls_DF$stateyrmn <- as.factor(Climate_Controls_DF$stateyrmn)\n",
    "# Climate_Controls_DF$userID <- as.factor(Climate_Controls_DF$randomized_id)\n",
    "# Climate_Controls_DF$state <- as.factor(Climate_Controls_DF$state)\n",
    "# Climate_Controls_DF$country <- as.factor(Climate_Controls_DF$country)\n",
    "# Climate_Controls_DF$sex <- as.factor(Climate_Controls_DF$sex)\n",
    "# Climate_Controls_DF$BMIcategory <- as.factor(Climate_Controls_DF$BMI_category)\n",
    "\n",
    "\n",
    "# #create trange climate variables for GHCND and NCEP Reanalysis\n",
    "# Climate_Controls_DF$trange <- (Climate_Controls_DF$tmax - Climate_Controls_DF$tmin)\n",
    "# Climate_Controls_DF$trange_norm_w7 <- (Climate_Controls_DF$tmax_norm_w7 - Climate_Controls_DF$tmin_norm_w7)\n",
    "# Climate_Controls_DF$trange_rean2 <- (Climate_Controls_DF$tmax_rean2 - Climate_Controls_DF$tmin_rean2)\n",
    "# Climate_Controls_DF$trange_norm_7_rean2 <- (Climate_Controls_DF$tmax_norm_7_rean2 - Climate_Controls_DF$tmin_norm_7_rean2)\n",
    "\n",
    "# #create climate anomaly variables for GHCND and for NCEP Reanalysis\n",
    "# Climate_Controls_DF$tanomaly_norm_w7 <- (Climate_Controls_DF$tmin - Climate_Controls_DF$tmin_norm_w7)\n",
    "# Climate_Controls_DF$tanomaly_norm_7_rean2 <- (Climate_Controls_DF$tmin_rean2 - Climate_Controls_DF$tmin_norm_7_rean2)\n",
    "# Climate_Controls_DF$prcpanomaly_norm_w7 <- (Climate_Controls_DF$prcp - Climate_Controls_DF$prcp_norm_w7)\n",
    "# Climate_Controls_DF$prcpanomaly_norm_7_rean2 <- (Climate_Controls_DF$prcp_rean2 - Climate_Controls_DF$prcp_norm_7_rean2)\n",
    "\n",
    "# #cut tmin for flexible model\n",
    "# Climate_Controls_DF$CUTTMIN <- cut(Climate_Controls_DF$tmin, breaks=c(-Inf,seq(-20, 30, by=5),Inf)) \n",
    "# Climate_Controls_DF$CUTTMIN <- relevel(Climate_Controls_DF$CUTTMIN, ref=\"(5,10]\")\n",
    "# Climate_Controls_DF$CUTTMIN_rean2 <- cut(Climate_Controls_DF$tmin_rean2, breaks=c(-Inf,seq(-20, 30, by=5),Inf)) \n",
    "# Climate_Controls_DF$CUTTMIN_rean2 <- relevel(Climate_Controls_DF$CUTTMIN_rean2, ref=\"(5,10]\")\n",
    "# #cut tmin_norm for flexible model \n",
    "# Climate_Controls_DF$CUTTMIN.NORM1981_2010 <- cut(Climate_Controls_DF$tmin_norm_w7, breaks=c(-Inf,seq(-20, 30, by=5),Inf)) \n",
    "# Climate_Controls_DF$CUTTMIN.NORM1981_2010 <- relevel(Climate_Controls_DF$CUTTMIN.NORM1981_2010, ref=\"(5,10]\")\n",
    "# Climate_Controls_DF$CUTTMIN.NORM1981_2010_rean2 <- cut(Climate_Controls_DF$tmin_norm_7_rean2, breaks=c(-Inf,seq(-20, 30, by=5),Inf)) \n",
    "# Climate_Controls_DF$CUTTMIN.NORM1981_2010_rean2 <- relevel(Climate_Controls_DF$CUTTMIN.NORM1981_2010_rean2, ref=\"(5,10]\")\n",
    "\n",
    "# #cut trange for flexible model\n",
    "# Climate_Controls_DF$CUTTRANGE <- cut(Climate_Controls_DF$trange, breaks=c(-Inf,seq(0, 20, by=5),Inf))\n",
    "# Climate_Controls_DF$CUTTRANGE <- relevel(Climate_Controls_DF$CUTTRANGE, ref=\"(5,10]\")\n",
    "# Climate_Controls_DF$CUTTRANGE_rean2 <- cut(Climate_Controls_DF$trange_rean2, breaks=c(-Inf,seq(0, 20, by=5),Inf))\n",
    "# Climate_Controls_DF$CUTTRANGE_rean2 <- relevel(Climate_Controls_DF$CUTTRANGE_rean2, ref=\"(5,10]\")\n",
    "# #cut trange_norm for demeaning series for spline projections\n",
    "# Climate_Controls_DF$CUTTRANGE.NORM1981_2010 <- cut(Climate_Controls_DF$trange_norm_w7, breaks=c(-Inf,seq(0, 20, by=5),Inf)) \n",
    "# Climate_Controls_DF$CUTTRANGE.NORM1981_2010 <- relevel(Climate_Controls_DF$CUTTRANGE.NORM1981_2010, ref=\"(5,10]\")\n",
    "# Climate_Controls_DF$CUTTRANGE.NORM1981_2010_rean2 <- cut(Climate_Controls_DF$trange_norm_7_rean2, breaks=c(-Inf,seq(0, 20, by=5),Inf)) \n",
    "# Climate_Controls_DF$CUTTRANGE.NORM1981_2010_rean2 <- relevel(Climate_Controls_DF$CUTTRANGE.NORM1981_2010_rean2, ref=\"(5,10]\")\n",
    "\n",
    "# #cut tanomaly for flexanomaly model \n",
    "# Climate_Controls_DF$cuttanomaly <- cut(Climate_Controls_DF$tanomaly_norm_w7, breaks=c(-Inf,seq(-10.5, 10.5, by=1),Inf)) \n",
    "# Climate_Controls_DF$cuttanomaly <- relevel(Climate_Controls_DF$cuttanomaly, ref=\"(-0.5,0.5]\")\n",
    "# Climate_Controls_DF$cuttanomaly_rean2 <- cut(Climate_Controls_DF$tanomaly_norm_7_rean2, breaks=c(-Inf,seq(-10.5, 10.5, by=1),Inf)) \n",
    "# Climate_Controls_DF$cuttanomaly_rean2 <- relevel(Climate_Controls_DF$cuttanomaly_rean2, ref=\"(-0.5,0.5]\")\n",
    "\n",
    "# #cut prcp_anomaly for flexanomaly model\n",
    "# Climate_Controls_DF$cutprcpanomaly <- cut(Climate_Controls_DF$prcpanomaly_norm_w7, breaks=c(-Inf,seq(0, 3, by=1),Inf)) \n",
    "# Climate_Controls_DF$cutprcpanomaly <- relevel(Climate_Controls_DF$cutprcpanomaly, ref=\"(-Inf,0]\")\n",
    "# Climate_Controls_DF$cutprcpanomaly_rean2 <- cut(Climate_Controls_DF$prcpanomaly_norm_7_rean2, breaks=c(-Inf,seq(0, 3, by=1),Inf)) \n",
    "# Climate_Controls_DF$cutprcpanomaly_rean2 <- relevel(Climate_Controls_DF$cutprcpanomaly_rean2, ref=\"(-Inf,0]\")\n",
    "\n",
    "# #cut prcp for flexible model\n",
    "# Climate_Controls_DF$CUTPRCP <- cut(Climate_Controls_DF$prcp, breaks=c(-Inf,seq(0, 3, by=1),Inf)) \n",
    "# Climate_Controls_DF$CUTPRCP <- relevel(Climate_Controls_DF$CUTPRCP, ref=\"(-Inf,0]\")\n",
    "# Climate_Controls_DF$CUTPRCP_rean2 <- cut(Climate_Controls_DF$prcp_rean2, breaks=c(-Inf,seq(0, 3, by=1),Inf)) \n",
    "# Climate_Controls_DF$CUTPRCP_rean2 <- relevel(Climate_Controls_DF$CUTPRCP_rean2, ref=\"(-Inf,0]\")\n",
    "# #cut prcp_norm for demeaning series for spline projections\n",
    "# Climate_Controls_DF$CUTPRCP.NORM1981_2010 <- cut(Climate_Controls_DF$prcp_norm_w7, breaks=c(-Inf,seq(0, 3, by=1),Inf)) \n",
    "# Climate_Controls_DF$CUTPRCP.NORM1981_2010 <- relevel(Climate_Controls_DF$CUTPRCP.NORM1981_2010, ref=\"(-Inf,0]\")\n",
    "# Climate_Controls_DF$CUTPRCP.NORM1981_2010_rean2 <- cut(Climate_Controls_DF$prcp_norm_7_rean2, breaks=c(-Inf,seq(0, 3, by=1),Inf)) \n",
    "# Climate_Controls_DF$CUTPRCP.NORM1981_2010_rean2 <- relevel(Climate_Controls_DF$CUTPRCP.NORM1981_2010_rean2, ref=\"(-Inf,0]\")\n",
    "\n",
    "# #cut wind for flexible model\n",
    "# Climate_Controls_DF$CUTWIND_rean2 <- cut(Climate_Controls_DF$wind_rean2, breaks=c(seq(0, 10, by=5),Inf)) \n",
    "# Climate_Controls_DF$CUTWIND_rean2 <- relevel(Climate_Controls_DF$CUTWIND_rean2, ref=\"(0,5]\")\n",
    "# #cut wind_norm for demeaning series for spline projections\n",
    "# Climate_Controls_DF$CUTWIND.NORM1981_2010_rean2 <- cut(Climate_Controls_DF$wind_norm_7_rean2, breaks=c(seq(0, 10, by=5),Inf)) \n",
    "# Climate_Controls_DF$CUTWIND.NORM1981_2010_rean2 <- relevel(Climate_Controls_DF$CUTWIND.NORM1981_2010_rean2, ref=\"(0,5]\")\n",
    "\n",
    "# #cut rhum for flexible model\n",
    "# Climate_Controls_DF$CUTRHUM_rean2 <- cut(Climate_Controls_DF$rhum_rean2, breaks=c(seq(0, 100, by=20))) \n",
    "# Climate_Controls_DF$CUTRHUM_rean2 <- relevel(Climate_Controls_DF$CUTRHUM_rean2, ref=\"(60,80]\")\n",
    "# #cut rhum_norm for demeaning series for spline projections\n",
    "# Climate_Controls_DF$CUTRHUM.NORM1981_2010_rean2 <- cut(Climate_Controls_DF$rhum_norm_7_rean2, breaks=c(seq(0, 100, by=20))) \n",
    "# Climate_Controls_DF$CUTRHUM.NORM1981_2010_rean2 <- relevel(Climate_Controls_DF$CUTRHUM.NORM1981_2010_rean2, ref=\"(60,80]\")\n",
    "\n",
    "# #cut cloud for flexible model\n",
    "# Climate_Controls_DF$CUTCLOUD_rean2 <- cut(Climate_Controls_DF$cloud_rean2, breaks=c(-Inf,seq(0, 100, by=20))) \n",
    "# Climate_Controls_DF$CUTCLOUD_rean2 <- relevel(Climate_Controls_DF$CUTCLOUD_rean2, ref=\"(-Inf,0]\")\n",
    "# #cut cloud_norm for demeaning series for spline projections\n",
    "# Climate_Controls_DF$CUTCLOUD.NORM1981_2010_rean2 <- cut(Climate_Controls_DF$cloud_norm_7_rean2, breaks=c(-Inf,seq(0, 100, by=20))) \n",
    "# Climate_Controls_DF$CUTCLOUD.NORM1981_2010_rean2 <- relevel(Climate_Controls_DF$CUTCLOUD.NORM1981_2010_rean2, ref=\"(-Inf,0]\")\n",
    "\n",
    "# #cut heat.index for heat.index flexible model\n",
    "# Climate_Controls_DF$cutheat.index <- cut(Climate_Controls_DF$heat.index, breaks=c(-Inf,seq(-15, 25, by=5),Inf)) \n",
    "# Climate_Controls_DF$cutheat.index <- relevel(Climate_Controls_DF$cutheat.index, ref=\"(5,10]\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#FIG 1A - Plot World Map of weather stations from NOAA's Global Historical Climatology Network - Daily (GHCND)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BSKadOmffnll/7eJXA9tsg1EpgI72FHhLvchZEqx40bJ/RVHzJkiMdo/sL9NYSQ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xJZmhBa5VbfVl+Qmv07WNcgHN8ujLd6t1CMk5OHKWJ5o8F36qE3CUKIcKId6/KUeKtn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\nYXe2+O7B0Mb+1JYBKSvsz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GgXZ76660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\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QBBtNnfxMZDXLRn0bXX3/PEP1wcdVU2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IZCiv7c4bDdIi8Y5t4XViKKdu/KK17ZsD75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\niGrjwAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#get world map and join on country as region\n",
    "map.world <- map_data(\"world\")\n",
    "\n",
    "#load NOAA weather stations\n",
    "station.points <- read.csv(\"model_data/Fig1_GHCND_stations.csv\", fill=TRUE, header=TRUE, sep=\",\")\n",
    "\n",
    "#plot worldmap of stations\n",
    "map.plot<-ggplot() +\n",
    "  geom_polygon(data = map.world, aes(x = long, y = lat, group = group)) + \n",
    "  coord_fixed(1.5) +\n",
    "  geom_point(data = station.points, aes(x = lon, y = lat), color = \"#0099CC\", size = .1, alpha = .5) +\n",
    "  scale_fill_gradient(na.value = \"white\", low = \"#66CC66\", high = \"#003300\", guide = \"colourbar\", aesthetics = \"fill\", trans = \"log10\") +\n",
    "  labs(title = 'Country level coverage of NOAA GHCND weather stations and sleeptracking wristband users')\n",
    "map.plot\n",
    "\n",
    "#ggsave(map.plot, \"weather_map.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#FIG 1B - Plot World Map of country-level count of sleep-tracking wristband subjects in study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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di5CIKd07kl2IkLbGrnq0oOIn6UbN2O/TgKInBihDILpr4aMnkiVOlOEuOcEO8i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lFOky0SPTFpJPgh1EUcsBri6H7EwkmsDa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\nHQBAIAQ7AIBACHYAAIEQ7AAAAvH/Aaw6v6RADtOuAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# setDT(Climate_Controls_DF)\n",
    "# #count participants per country\n",
    "# country_df<-Climate_Controls_DF[,.(usercount=uniqueN(userID)),by=.(country)]\n",
    "# map.world_joined <- left_join(map.world, country_df, by = c('region' = 'country'))\n",
    "\n",
    "# #plot worldmap of countries with countries colored by # of users\n",
    "# map.plot<-ggplot() +\n",
    "#   geom_polygon(data = map.world_joined, aes(x = long, y = lat, group = group, fill = usercount)) + \n",
    "#   coord_fixed(1.5) +\n",
    "#   #geom_point(data = station.points, aes(x = lon, y = lat), color = \"#0099CC\", size = .5, alpha = .1) +\n",
    "#   scale_fill_gradient(na.value = \"grey90\", low = \"#66CC66\", high = \"#003300\", guide = \"colourbar\", aesthetics = \"fill\", trans = \"log10\") +\n",
    "#   labs(title = 'Count of sleep-tracking wristband users') + theme_minimal()\n",
    "# map.plot\n",
    "\n",
    "# # #ggsave(map.plot, \"density_map.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#FIG 1C - Plot Avg. Sociotemporal Deviations for 2016 after de-meaning individual averages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Plot average sleep deviation by day of year in 2016\n",
    "# Climate_Controls_DF[,average:=mean(sleep_duration,na.rm=T),by=.(userID)]\n",
    "# Climate_Controls_DF[,daily_deviation:= sleep_duration - average]\n",
    "# Climate_Controls_DF[,daily_deviation:= mean(daily_deviation,na.rm=T),by=.(date)]\n",
    "# time_plot<-Climate_Controls_DF[year==\"2016\"]\n",
    "# write.csv(time_plot,file=\"model_data/Fig1C_time_plot.csv\")\n",
    "\n",
    "#See separate jupyter notebook '2016_sleep_deviation_plot_code.ipynb' for python plotting script"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#FIG 1D - Plot Total count of obs by year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HHG9OnToyiaMWNGQ0PD/rkDAABfBAfi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\nAQAAGMR/AeE9zmePlbE3AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #count # of nights after filtering by year\n",
    "# setDF(Climate_Controls_DF)\n",
    "# Climate_Controls_DF_2015 <- subset(Climate_Controls_DF, (year == 2015))\n",
    "# Climate_Controls_DF_2016 <- subset(Climate_Controls_DF, (year == 2016))\n",
    "# Climate_Controls_DF_2017 <- subset(Climate_Controls_DF, (year == 2017))\n",
    "# count_15 <- as.numeric(nrow(Climate_Controls_DF_2015))\n",
    "# count_16 <- as.numeric(nrow(Climate_Controls_DF_2016))\n",
    "# count_17 <- as.numeric(nrow(Climate_Controls_DF_2017))\n",
    "\n",
    "# observations <- c(count_15, count_16, count_17)\n",
    "# year <- c('2015', '2016', '2017')\n",
    "# total_obs <- data.frame(observations,year)\n",
    "# write.csv(total_obs, file=\"model_data/Fig1D_obs_plot.csv\")\n",
    "\n",
    "total_obs<-read.csv(\"model_data/Fig1D_obs_plot.csv\")\n",
    "# total obs barplot\n",
    "bar.plot<-ggplot(data=total_obs, aes(x=year, y=observations)) +\n",
    "  geom_bar(stat=\"identity\", fill=\"steelblue\")+\n",
    "  geom_text(aes(label=observations), vjust=1.6, color=\"white\", size=3.5) + theme_minimal()\n",
    "#ggsave(\"barplot_obs.pdf\")\n",
    "bar.plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sleep Duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#FIG 2A - Temperature vs. Sleep Duration FE Reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ CUTTMIN + tmin_norm_w7 +      CUTPRCP + prcp_norm_w7 + CUTTRANGE + trange_norm_w7 + CUTCLOUD_rean2 +      cloud_norm_7_rean2 + CUTRHUM_rean2 + rhum_norm_7_rean2 +      CUTWIND_rean2 + wind_norm_7_rean2 | userID + unique_day_no +      stateyrmn | 0 | state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-383.36  -49.12   -2.32   45.76  409.00 \n",
       "\n",
       "Coefficients:\n",
       "                         Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "CUTTMIN(-Inf,-20]        3.315982     1.456368   2.277 0.022793 *  \n",
       "CUTTMIN(-20,-15]         3.092955     0.865977   3.572 0.000355 ***\n",
       "CUTTMIN(-15,-10]         2.018219     0.670400   3.010 0.002608 ** \n",
       "CUTTMIN(-10,-5]          0.866649     0.476936   1.817 0.069199 .  \n",
       "CUTTMIN(-5,0]            0.427188     0.392325   1.089 0.276214    \n",
       "CUTTMIN(0,5]             0.657996     0.247692   2.657 0.007895 ** \n",
       "CUTTMIN(10,15]          -1.955992     0.219309  -8.919  < 2e-16 ***\n",
       "CUTTMIN(15,20]          -4.378857     0.333813 -13.118  < 2e-16 ***\n",
       "CUTTMIN(20,25]          -6.397027     0.449722 -14.224  < 2e-16 ***\n",
       "CUTTMIN(25,30]          -7.242957     0.605210 -11.968  < 2e-16 ***\n",
       "CUTTMIN(30, Inf]       -10.766352     1.769035  -6.086 1.16e-09 ***\n",
       "tmin_norm_w7            -0.208332     0.078878  -2.641 0.008262 ** \n",
       "CUTPRCP(0,1]             0.297852     0.105839   2.814 0.004890 ** \n",
       "CUTPRCP(1,2]             1.217931     0.291786   4.174 2.99e-05 ***\n",
       "CUTPRCP(2,3]             1.329769     0.350353   3.796 0.000147 ***\n",
       "CUTPRCP(3, Inf]          2.105703     0.540594   3.895 9.81e-05 ***\n",
       "prcp_norm_w7             2.162832     1.096333   1.973 0.048520 *  \n",
       "CUTTRANGE(-Inf,0]        2.690380     1.212489   2.219 0.026494 *  \n",
       "CUTTRANGE(0,5]           0.261373     0.175588   1.489 0.136604    \n",
       "CUTTRANGE(10,15]        -0.589632     0.113054  -5.216 1.83e-07 ***\n",
       "CUTTRANGE(15,20]        -2.028818     0.298971  -6.786 1.15e-11 ***\n",
       "CUTTRANGE(20, Inf]      -2.124465     0.725820  -2.927 0.003423 ** \n",
       "trange_norm_w7          -0.029209     0.145130  -0.201 0.840495    \n",
       "CUTCLOUD_rean2(0,20]     0.522354     0.438448   1.191 0.233508    \n",
       "CUTCLOUD_rean2(20,40]    0.381588     0.460814   0.828 0.407629    \n",
       "CUTCLOUD_rean2(40,60]    0.657887     0.453120   1.452 0.146528    \n",
       "CUTCLOUD_rean2(60,80]    1.001414     0.482427   2.076 0.037914 *  \n",
       "CUTCLOUD_rean2(80,100]   1.624378     0.457772   3.548 0.000388 ***\n",
       "cloud_norm_7_rean2      -0.030111     0.023238  -1.296 0.195058    \n",
       "CUTRHUM_rean2(0,20]     -4.675622     1.249795  -3.741 0.000183 ***\n",
       "CUTRHUM_rean2(20,40]    -1.479920     0.624889  -2.368 0.017870 *  \n",
       "CUTRHUM_rean2(40,60]     0.008832     0.191310   0.046 0.963177    \n",
       "CUTRHUM_rean2(80,100]   -0.327544     0.135601  -2.415 0.015714 *  \n",
       "rhum_norm_7_rean2        0.101106     0.039281   2.574 0.010055 *  \n",
       "CUTWIND_rean2(5,10]      0.483949     0.117773   4.109 3.97e-05 ***\n",
       "CUTWIND_rean2(10,Inf]    0.779209     0.230403   3.382 0.000720 ***\n",
       "wind_norm_7_rean2       -0.323516     0.193869  -1.669 0.095171 .  \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.71 on 4345327 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2842   Adjusted R-squared: 0.2744 \n",
       "Multiple R-squared(proj model): 0.0001864   Adjusted R-squared: -0.01358 \n",
       "F-statistic(full model, *iid*):28.83 on 59843 and 4345327 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 20.36 on 37 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #Binned FE Regression GHCND - tmin\n",
    "# flex_t_binned_GHCND <- felm(formula = sleep_duration_minutes ~ CUTTMIN + tmin_norm_w7 + \n",
    "#                                           CUTPRCP + prcp_norm_w7 + \n",
    "#                                           CUTTRANGE + trange_norm_w7 + \n",
    "#                                           CUTCLOUD_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           CUTRHUM_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           CUTWIND_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(flex_t_binned_GHCND)\n",
    "\n",
    "# #save model coefficients for plotting\n",
    "# dt <- as.data.frame(summary(flex_t_binned_GHCND)$coefficients[, 1:4]) # extract from felm summary (use df to keep coefficient\n",
    "# write.csv(dt,file=\"model_data/Fig2A_binned_tmin_sleep_duration_model_output.csv\")#save data as CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 2274874 rows containing non-finite values (stat_bin).”Warning message:\n",
      "“Removed 2 rows containing missing values (geom_bar).”"
     ]
    },
    {
     "data": {
      "image/png": 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SMjI/jPsbm6YjUazdy5c/fs2eN+dMWK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\nvHnzxowZU9uQILBYpjRC8SmRZLQ8Sd6z1PLJ9fOyUoRqO9mJ5B0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fPb/kXzdq7u0RMULBRGbZDm2qfL9fh8Fzp30ZEcsqExhFDCNTN55x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Onc5ucAh2VZ8HDx68fv36oKAg+QXUPQQ7AACo5wKFsRwAACAASURBVHyY7aror4iZ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\n2KdPH1Udf3//Zs2aMXai7JWmcunSJUaFuLg4x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epiwzDW1tYmJiYkSerq6rLZbIqiVHzK7Ovry+Vy4+PjtRoNBAK5XE6W5W8NqB4BZtfP\nmc3meDxeKBQCgUDDYkmStra2DMNYWFhoQ9/QEZUHCQNAKzw9PaXT6Ww2axhGuVx+fX0dHBxU\nVXVoaMjn883OzjY8ArU9IpHI/v7+xysXFxfT09PlcjmVSp2cnNzc3Dw/PzudzpGRkcXFxaWl\npWbiQpuHf3l5eXh4WCqVrq+vX15eXC7XzMzM6uqq2+3+W5BMJitW6RRF2d7e/naLb29vx8fH\nmUzm6urq4eGhu7t7YGDA4/GsrKz4/X6TyVQsFvf29j4+Mjc3FwqFvt3iR//L7Gq1u7u7s7Oz\n8/Pz29vb+/v7x8fH3t5eu92uKIrH45mamvo9r+I3I9gBwD+1gl2n+gMAX8KnWAAAAEEQ7AAA\nAARBsAMAABAEwQ4AAEAQBDsAAABBEOwAAAAEQbADAAAQBMEOAABAEGxQDAAAIAhW7AAAAARB\nsAMAABAEwQ4AAEAQfwD3udiON01q4wAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#load saved model output\n",
    "dt <- read.csv(file=\"model_data/Fig2A_binned_tmin_sleep_duration_model_output.csv\",header=T)\n",
    "flex.plot <- binned.plot(felm.est = dt,\n",
    "                 plotvar = \"CUTTMIN\",\n",
    "                 breaks = 5,\n",
    "                 omit = c(5,10), \n",
    "                 linecolor = \"purple\",\n",
    "                 pointfill = \"purple\",\n",
    "                 errorfill = \"purple\",\n",
    "                 xlabel = \"Min. Temperature in C\",\n",
    "                 ylabel = \"Change in Sleep Duration (Minutes)\"\n",
    "                 )\n",
    "# hist <- axis_canvas(flex.plot, axis = \"x\") + \n",
    "#         geom_histogram(data = Climate_Controls_DF, aes(x = Climate_Controls_DF$tmin), bins=41, fill='gray60', color='gray60', alpha=0.75, size=0.1)\n",
    "# flex.plot <- insert_xaxis_grob(flex.plot, hist, position = \"bottom\", height = grid::unit(0.1, \"null\"))\n",
    "flex.plot <- ggdraw(flex.plot)\n",
    "#save_plot(\"flexplot_binned_GHCND.pdf\", flex.plot)\n",
    "flex.plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{Binned Nighttime Minimum Temperature and Sleep Duration FE Regressions} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{1}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-2} \n",
      " & Binned GHCND \\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " CUTTMIN(-Inf,-20] & 3.316$^{**}$ \\\\ \n",
      "  & (1.456) \\\\ \n",
      "  CUTTMIN(-20,-15] & 3.093$^{***}$ \\\\ \n",
      "  & (0.866) \\\\ \n",
      "  CUTTMIN(-15,-10] & 2.018$^{***}$ \\\\ \n",
      "  & (0.670) \\\\ \n",
      "  CUTTMIN(-10,-5] & 0.867$^{*}$ \\\\ \n",
      "  & (0.477) \\\\ \n",
      "  CUTTMIN(-5,0] & 0.427 \\\\ \n",
      "  & (0.392) \\\\ \n",
      "  CUTTMIN(0,5] & 0.658$^{***}$ \\\\ \n",
      "  & (0.248) \\\\ \n",
      "  CUTTMIN(10,15] & $-$1.956$^{***}$ \\\\ \n",
      "  & (0.219) \\\\ \n",
      "  CUTTMIN(15,20] & $-$4.379$^{***}$ \\\\ \n",
      "  & (0.334) \\\\ \n",
      "  CUTTMIN(20,25] & $-$6.397$^{***}$ \\\\ \n",
      "  & (0.450) \\\\ \n",
      "  CUTTMIN(25,30] & $-$7.243$^{***}$ \\\\ \n",
      "  & (0.605) \\\\ \n",
      "  CUTTMIN(30, Inf] & $-$10.766$^{***}$ \\\\ \n",
      "  & (1.769) \\\\ \n",
      "  tmin\\_norm\\_w7 & $-$0.208$^{***}$ \\\\ \n",
      "  & (0.079) \\\\ \n",
      "  CUTPRCP(0,1] & 0.298$^{***}$ \\\\ \n",
      "  & (0.106) \\\\ \n",
      "  CUTPRCP(1,2] & 1.218$^{***}$ \\\\ \n",
      "  & (0.292) \\\\ \n",
      "  CUTPRCP(2,3] & 1.330$^{***}$ \\\\ \n",
      "  & (0.350) \\\\ \n",
      "  CUTPRCP(3, Inf] & 2.106$^{***}$ \\\\ \n",
      "  & (0.541) \\\\ \n",
      "  prcp\\_norm\\_w7 & 2.163$^{**}$ \\\\ \n",
      "  & (1.096) \\\\ \n",
      "  CUTTRANGE(-Inf,0] & 2.690$^{**}$ \\\\ \n",
      "  & (1.212) \\\\ \n",
      "  CUTTRANGE(0,5] & 0.261 \\\\ \n",
      "  & (0.176) \\\\ \n",
      "  CUTTRANGE(10,15] & $-$0.590$^{***}$ \\\\ \n",
      "  & (0.113) \\\\ \n",
      "  CUTTRANGE(15,20] & $-$2.029$^{***}$ \\\\ \n",
      "  & (0.299) \\\\ \n",
      "  CUTTRANGE(20, Inf] & $-$2.124$^{***}$ \\\\ \n",
      "  & (0.726) \\\\ \n",
      "  trange\\_norm\\_w7 & $-$0.029 \\\\ \n",
      "  & (0.145) \\\\ \n",
      "  CUTCLOUD\\_rean2(0,20] & 0.522 \\\\ \n",
      "  & (0.438) \\\\ \n",
      "  CUTCLOUD\\_rean2(20,40] & 0.382 \\\\ \n",
      "  & (0.461) \\\\ \n",
      "  CUTCLOUD\\_rean2(40,60] & 0.658 \\\\ \n",
      "  & (0.453) \\\\ \n",
      "  CUTCLOUD\\_rean2(60,80] & 1.001$^{**}$ \\\\ \n",
      "  & (0.482) \\\\ \n",
      "  CUTCLOUD\\_rean2(80,100] & 1.624$^{***}$ \\\\ \n",
      "  & (0.458) \\\\ \n",
      "  cloud\\_norm\\_7\\_rean2 & $-$0.030 \\\\ \n",
      "  & (0.023) \\\\ \n",
      "  CUTRHUM\\_rean2(0,20] & $-$4.676$^{***}$ \\\\ \n",
      "  & (1.250) \\\\ \n",
      "  CUTRHUM\\_rean2(20,40] & $-$1.480$^{**}$ \\\\ \n",
      "  & (0.625) \\\\ \n",
      "  CUTRHUM\\_rean2(40,60] & 0.009 \\\\ \n",
      "  & (0.191) \\\\ \n",
      "  CUTRHUM\\_rean2(80,100] & $-$0.328$^{**}$ \\\\ \n",
      "  & (0.136) \\\\ \n",
      "  rhum\\_norm\\_7\\_rean2 & 0.101$^{**}$ \\\\ \n",
      "  & (0.039) \\\\ \n",
      "  CUTWIND\\_rean2(5,10] & 0.484$^{***}$ \\\\ \n",
      "  & (0.118) \\\\ \n",
      "  CUTWIND\\_rean2(10,Inf] & 0.779$^{***}$ \\\\ \n",
      "  & (0.230) \\\\ \n",
      "  wind\\_norm\\_7\\_rean2 & $-$0.324$^{*}$ \\\\ \n",
      "  & (0.194) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes \\\\ \n",
      "Date FE & Yes \\\\ \n",
      "State:Month FE & Yes \\\\ \n",
      "Observations & 4,405,171 \\\\ \n",
      "R$^{2}$ & 0.284 \\\\ \n",
      "Adjusted R$^{2}$ & 0.274 \\\\ \n",
      "Residual Std. Error & 77.707 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{1}{r}{Standard errors are in parentheses and are clustered on the first admin level} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# invisible(stargazer(flex_t_binned_GHCND,\n",
    "#           type='latex',\n",
    "#           title = \"Binned Nighttime Minimum Temperature and Sleep Duration FE Regressions\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Binned GHCND'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the first admin level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>CUTTMIN</th><th scope=col>total</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>(-Inf,-20]</td><td>  11644   </td></tr>\n",
       "\t<tr><td>(-20,-15] </td><td>  16230   </td></tr>\n",
       "\t<tr><td>(-15,-10] </td><td>  39701   </td></tr>\n",
       "\t<tr><td>(-10,-5]  </td><td> 128603   </td></tr>\n",
       "\t<tr><td>(-5,0]    </td><td> 506951   </td></tr>\n",
       "\t<tr><td>(0,5]     </td><td>1010083   </td></tr>\n",
       "\t<tr><td>(5,10]    </td><td> 772896   </td></tr>\n",
       "\t<tr><td>(10,15]   </td><td> 760679   </td></tr>\n",
       "\t<tr><td>(15,20]   </td><td> 532595   </td></tr>\n",
       "\t<tr><td>(20,25]   </td><td> 489756   </td></tr>\n",
       "\t<tr><td>(25,30]   </td><td> 135311   </td></tr>\n",
       "\t<tr><td>(30, Inf] </td><td>    722   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " CUTTMIN & total\\\\\n",
       "\\hline\n",
       "\t (-Inf,-20{]} &   11644     \\\\\n",
       "\t (-20,-15{]}  &   16230     \\\\\n",
       "\t (-15,-10{]}  &   39701     \\\\\n",
       "\t (-10,-5{]}   &  128603     \\\\\n",
       "\t (-5,0{]}     &  506951     \\\\\n",
       "\t (0,5{]}      & 1010083     \\\\\n",
       "\t (5,10{]}     &  772896     \\\\\n",
       "\t (10,15{]}    &  760679     \\\\\n",
       "\t (15,20{]}    &  532595     \\\\\n",
       "\t (20,25{]}    &  489756     \\\\\n",
       "\t (25,30{]}    &  135311     \\\\\n",
       "\t (30, Inf{]}  &     722     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| CUTTMIN | total |\n",
       "|---|---|\n",
       "| (-Inf,-20] |   11644    |\n",
       "| (-20,-15]  |   16230    |\n",
       "| (-15,-10]  |   39701    |\n",
       "| (-10,-5]   |  128603    |\n",
       "| (-5,0]     |  506951    |\n",
       "| (0,5]      | 1010083    |\n",
       "| (5,10]     |  772896    |\n",
       "| (10,15]    |  760679    |\n",
       "| (15,20]    |  532595    |\n",
       "| (20,25]    |  489756    |\n",
       "| (25,30]    |  135311    |\n",
       "| (30, Inf]  |     722    |\n",
       "\n"
      ],
      "text/plain": [
       "   CUTTMIN    total  \n",
       "1  (-Inf,-20]   11644\n",
       "2  (-20,-15]    16230\n",
       "3  (-15,-10]    39701\n",
       "4  (-10,-5]    128603\n",
       "5  (-5,0]      506951\n",
       "6  (0,5]      1010083\n",
       "7  (5,10]      772896\n",
       "8  (10,15]     760679\n",
       "9  (15,20]     532595\n",
       "10 (20,25]     489756\n",
       "11 (25,30]     135311\n",
       "12 (30, Inf]      722"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>total</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>  29702</td></tr>\n",
       "\t<tr><td>  47400</td></tr>\n",
       "\t<tr><td> 100232</td></tr>\n",
       "\t<tr><td> 262332</td></tr>\n",
       "\t<tr><td> 750659</td></tr>\n",
       "\t<tr><td>1304568</td></tr>\n",
       "\t<tr><td>1273489</td></tr>\n",
       "\t<tr><td>1269646</td></tr>\n",
       "\t<tr><td>1066543</td></tr>\n",
       "\t<tr><td> 868785</td></tr>\n",
       "\t<tr><td> 195586</td></tr>\n",
       "\t<tr><td>   5670</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|l}\n",
       " total\\\\\n",
       "\\hline\n",
       "\t   29702\\\\\n",
       "\t   47400\\\\\n",
       "\t  100232\\\\\n",
       "\t  262332\\\\\n",
       "\t  750659\\\\\n",
       "\t 1304568\\\\\n",
       "\t 1273489\\\\\n",
       "\t 1269646\\\\\n",
       "\t 1066543\\\\\n",
       "\t  868785\\\\\n",
       "\t  195586\\\\\n",
       "\t    5670\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| total |\n",
       "|---|\n",
       "|   29702 |\n",
       "|   47400 |\n",
       "|  100232 |\n",
       "|  262332 |\n",
       "|  750659 |\n",
       "| 1304568 |\n",
       "| 1273489 |\n",
       "| 1269646 |\n",
       "| 1066543 |\n",
       "|  868785 |\n",
       "|  195586 |\n",
       "|    5670 |\n",
       "\n"
      ],
      "text/plain": [
       "   total  \n",
       "1    29702\n",
       "2    47400\n",
       "3   100232\n",
       "4   262332\n",
       "5   750659\n",
       "6  1304568\n",
       "7  1273489\n",
       "8  1269646\n",
       "9  1066543\n",
       "10  868785\n",
       "11  195586\n",
       "12    5670"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #count sleep observations by temperature bin for observations with GHCND weather station data vs. observations with gridded REAN2 data \n",
    "\n",
    "# #GHCND\n",
    "# #subset by cols\n",
    "# setDT(Climate_Controls_DF)\n",
    "# cols <- c('userID', 'sleep_duration_minutes','CUTTMIN','tmin_norm_w7',\n",
    "#                                           'CUTPRCP', 'prcp_norm_w7',\n",
    "#                                           'CUTTRANGE', 'trange_norm_w7',\n",
    "#                                           'CUTCLOUD_rean2','cloud_norm_7_rean2', \n",
    "#                                           'CUTRHUM_rean2', 'rhum_norm_7_rean2', \n",
    "#                                           'CUTWIND_rean2', 'wind_norm_7_rean2')\n",
    "# weather<-Climate_Controls_DF[, .SD, .SDcols = cols]\n",
    "# weather<-na.omit(weather)\n",
    "# w<-weather[,.(total=.N,pct=.N/nrow(weather)),by=.(CUTTMIN)]\n",
    "# w[CUTTMIN=='(-Inf,-20]',ord:=\"1\"][CUTTMIN=='(-20,-15]',ord:=\"2\"][CUTTMIN=='(-15,-10]',ord:=\"3\"]\n",
    "# w[CUTTMIN=='(-10,-5]',ord:=\"4\"][CUTTMIN=='(-5,0]',ord:=\"5\"][CUTTMIN=='(0,5]',ord:=\"6\"][CUTTMIN=='(5,10]',ord:=\"7\"]\n",
    "# w[CUTTMIN=='(10,15]',ord:=\"8\"][CUTTMIN=='(15,20]',ord:=\"9\"][CUTTMIN=='(20,25]',ord:=\"10\"]\n",
    "# w[CUTTMIN=='(25,30]',ord:=\"11\"][CUTTMIN=='(30, Inf]',ord:=\"12\"]\n",
    "# w[,ord:=as.numeric(ord)]\n",
    "# w<-setorder(w,ord)\n",
    "# w[,1:2]\n",
    "\n",
    "# #REAN2\n",
    "# #subset by cols\n",
    "# cols <- c('userID', 'sleep_duration_minutes','CUTTMIN_rean2','tmin_norm_7_rean2',\n",
    "#                                           'CUTPRCP_rean2', 'prcp_norm_7_rean2',\n",
    "#                                           'CUTTRANGE_rean2', 'trange_norm_7_rean2',\n",
    "#                                           'CUTCLOUD_rean2','cloud_norm_7_rean2', \n",
    "#                                           'CUTRHUM_rean2', 'rhum_norm_7_rean2', \n",
    "#                                           'CUTWIND_rean2', 'wind_norm_7_rean2')\n",
    "# weather<-Climate_Controls_DF[, .SD, .SDcols = cols]\n",
    "# weather<-na.omit(weather)\n",
    "# w<-weather[,.(total=.N,pct=.N/nrow(weather)),by=.(CUTTMIN_rean2)]\n",
    "# w[CUTTMIN_rean2=='(-Inf,-20]',ord:=\"1\"][CUTTMIN_rean2=='(-20,-15]',ord:=\"2\"][CUTTMIN_rean2=='(-15,-10]',ord:=\"3\"]\n",
    "# w[CUTTMIN_rean2=='(-10,-5]',ord:=\"4\"][CUTTMIN_rean2=='(-5,0]',ord:=\"5\"][CUTTMIN_rean2=='(0,5]',ord:=\"6\"][CUTTMIN_rean2=='(5,10]',ord:=\"7\"]\n",
    "# w[CUTTMIN_rean2=='(10,15]',ord:=\"8\"][CUTTMIN_rean2=='(15,20]',ord:=\"9\"][CUTTMIN_rean2=='(20,25]',ord:=\"10\"]\n",
    "# w[CUTTMIN_rean2=='(25,30]',ord:=\"11\"][CUTTMIN_rean2=='(30, Inf]',ord:=\"12\"]\n",
    "# w[,ord:=as.numeric(ord)]\n",
    "# w<-setorder(w,ord)\n",
    "# w[,2]\n",
    "\n",
    "# setDF(Climate_Controls_DF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#FIG 2B - Temperature vs. Sleep Offset FE Reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = offset ~ CUTTMIN + tmin_norm_w7 + CUTPRCP + prcp_norm_w7 +      CUTTRANGE + trange_norm_w7 + CUTCLOUD_rean2 + cloud_norm_7_rean2 +      CUTRHUM_rean2 + rhum_norm_7_rean2 + CUTWIND_rean2 + wind_norm_7_rean2 |      userID + unique_day_no + stateyrmn | 0 | state, data = Climate_Controls_DF,      na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-670.08  -38.68   -2.79   36.05  618.20 \n",
       "\n",
       "Coefficients:\n",
       "                        Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "CUTTMIN(-Inf,-15]       2.971892     1.017179   2.922 0.003481 ** \n",
       "CUTTMIN(-15,-10]        0.369163     0.723815   0.510 0.610034    \n",
       "CUTTMIN(-10,-5]        -1.309613     0.524948  -2.495 0.012605 *  \n",
       "CUTTMIN(-5,0]          -1.018834     0.512091  -1.990 0.046640 *  \n",
       "CUTTMIN(0,5]           -0.475052     0.295608  -1.607 0.108047    \n",
       "CUTTMIN(10,15]         -0.002325     0.255485  -0.009 0.992738    \n",
       "CUTTMIN(15,20]         -1.160020     0.432946  -2.679 0.007376 ** \n",
       "CUTTMIN(20,25]         -2.116603     0.542083  -3.905 9.44e-05 ***\n",
       "CUTTMIN(25, Inf]       -2.510945     0.726823  -3.455 0.000551 ***\n",
       "tmin_norm_w7            0.068007     0.123916   0.549 0.583133    \n",
       "CUTPRCP(0,1]            0.921871     0.154980   5.948 2.71e-09 ***\n",
       "CUTPRCP(1,2]            1.973179     0.386090   5.111 3.21e-07 ***\n",
       "CUTPRCP(2,3]            2.689070     0.388409   6.923 4.41e-12 ***\n",
       "CUTPRCP(3, Inf]         1.839765     0.612718   3.003 0.002677 ** \n",
       "prcp_norm_w7            6.427095     1.321547   4.863 1.15e-06 ***\n",
       "CUTTRANGE(-Inf,0]       0.381484     1.541775   0.247 0.804574    \n",
       "CUTTRANGE(0,5]          0.432165     0.229523   1.883 0.059716 .  \n",
       "CUTTRANGE(10,15]       -0.532169     0.217262  -2.449 0.014308 *  \n",
       "CUTTRANGE(15,20]       -1.976748     0.380319  -5.198 2.02e-07 ***\n",
       "CUTTRANGE(20, Inf]     -2.727779     0.840273  -3.246 0.001169 ** \n",
       "trange_norm_w7         -0.022576     0.183804  -0.123 0.902243    \n",
       "CUTCLOUD_rean2(0,20]    0.340542     0.628606   0.542 0.587996    \n",
       "CUTCLOUD_rean2(20,40]  -0.081914     0.653402  -0.125 0.900234    \n",
       "CUTCLOUD_rean2(40,60]   0.080550     0.673393   0.120 0.904786    \n",
       "CUTCLOUD_rean2(60,80]   0.643870     0.691625   0.931 0.351878    \n",
       "CUTCLOUD_rean2(80,100]  1.781825     0.634513   2.808 0.004982 ** \n",
       "cloud_norm_7_rean2      0.052697     0.034398   1.532 0.125530    \n",
       "CUTRHUM_rean2(0,20]    -2.359865     2.097057  -1.125 0.260453    \n",
       "CUTRHUM_rean2(20,40]   -0.222059     0.938175  -0.237 0.812896    \n",
       "CUTRHUM_rean2(40,60]    0.163778     0.320334   0.511 0.609160    \n",
       "CUTRHUM_rean2(80,100]  -0.047227     0.223628  -0.211 0.832742    \n",
       "rhum_norm_7_rean2      -0.147294     0.054795  -2.688 0.007186 ** \n",
       "CUTWIND_rean2(5,10]     1.246165     0.157396   7.917 2.43e-15 ***\n",
       "CUTWIND_rean2(10,Inf]   2.322596     0.294103   7.897 2.85e-15 ***\n",
       "wind_norm_7_rean2      -0.235329     0.188974  -1.245 0.213022    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 75.81 on 4345329 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.4823   Adjusted R-squared: 0.4751 \n",
       "Multiple R-squared(proj model): 0.0003231   Adjusted R-squared: -0.01344 \n",
       "F-statistic(full model, *iid*):67.64 on 59841 and 4345329 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 21.63 on 35 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# flex_t_offset <- felm(formula = offset ~ CUTTMIN + tmin_norm_w7 + \n",
    "#                                           CUTPRCP + prcp_norm_w7 + \n",
    "#                                           CUTTRANGE + trange_norm_w7 + \n",
    "#                                           CUTCLOUD_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           CUTRHUM_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           CUTWIND_rean2 + wind_norm_7_rean2\n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(flex_t_offset)\n",
    "\n",
    "# #save model coefficients for plotting\n",
    "# dt <- as.data.frame(summary(flex_t_offset)$coefficients[, 1:4]) # extract from felm summary (use df to keep coefficient\n",
    "# write.csv(dt,file=\"model_data/Fig2B_binned_tmin_sleep_offset_model_output.csv\")#save data as CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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19eXt7q1avtx1FRUX/4wx+UlwEAANDG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jZC7eNeD6GACg4HNIfrEyiuN+tfib8LuV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\nurq6uvr11183utXHx0f/ySwh5NixY5s3b9Yfpi4uLm7atGkmTmG1HTt27Nq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cO3euFRVLSkqGDBkilUrN3F+lUo0fP3716tVWnEtLKpVO\nnDhxyZIlVh8BAADAFUSJuYRWBZUMD3vcxUt+zNnh/M3WzhNglHa45oiIiPDwcD8/v8LCwkeP\nHuXl5V2/fl2tVhvuf+3atRUrVnz00UdWnCs1NfWdd97RL4mJiRk9enSnTp3Cw8N5PF5RUdGl\nS5cOHDhw6dIlw+rZ2dlLly5dsGCBOecaP378zp07jW7icrlxcXEtWrQICQmRyWSPHj26cOFC\nVlaW0Z3nzJnj7e399ttvm3NSAAAA1xSl3kKUtwghQsmuGv5gZ4dDCCGENpsrxOAWDh065Ovr\na3TTw4cP582bZ7SLaHh4uEajMX1kw9Fk5s6d27x5c91q8+bNjx49Wlv1X375JTQ01PDU/v7+\nSqWyzuuq7RFqkyZNvvvuu8rKSsMq165dS0xMZLGMNAxzOJyLFy+aPmN6ejqjVv/+/euMEwAA\noJ4oH9L5r9MZ3rTiQXalQvufcyOyoPPEhQsXzNzTUt26dXPQkZ0iKSkpMTHRRL/U8vLy5557\n7o8//mCUnzt3Li4uzsSRDTtPCIVC3YPUAQMGHDp0yHRn5Ozs7G7duhUWFjLKjx8/3r9/fxMV\nHzx40KFDh8rKSkb522+/vXz5ctODp1y5cmXUqFEPHjxglLds2fL69esm6qLzBAAAuAFVPuH8\n3WSTU6V0bucJCx7Felj65UR+fn779u1r3bp1aWmpfnl6errpxM6QLqvr06fP4cOHGWmfoejo\n6M2bNw8fPpxRnpqaajqx++CDDwyzutWrVzOeAhvVuXPnixcvDho0KDU1Vb/8zp07W7duff31\n1+s8AgAAgOvi/PsgzulzkaHzhHMEBQW99NJLjML8/HzrjiYWi7dv315nVqc1bNiwDh06WHTq\n7OzsgwcPMgqnTJliTlanFRQUlJSUFBwczChftGiR0ZcOAQAAwApI7JzGMLsyPaqwCZ9++mlU\nVJT5+w8bNoxRUlZWZmL/devWMdKvyMjIVatWmX9GbZUvv/ySUZiTk3P27FmLjgMAAAC1sTWx\nU6lUt2/fPnTokOl9Vq5cmZSUdO/ePRtP50l8fHzschyhUDhp0iSLqrRu3dr8nVUq1ebNmxmF\nCxYssGLWihdeeKFTp06MQsO2QAAAALCO9cOdnDp1auvWrfv375dIJAKBoKamprY91Wr1+++/\nr11u3779yy+//NZbb5k/4ZinksvldjnOqFGj/Pz8LKoSEBBg/s5Xr15lNCWKxeIxY8ZYdEYt\niqISExMZb9r9+eefVhwKAAAADFnTYvfw4cMRI0b07dt3+/btEonEorrXrl2bNWtWTEzM0aNH\nrTi1J7lx44ZdjvPMM89YWoXNZpu/s+Gj0mHDhpn5Pp8hw6fAtY3tBwAAAJayOLG7cuVKx44d\njxw5YstZHzx4MGzYsIY8tVROTs73339vl0N1797dLsepzZkzZ+x4xmbNmnG5/+kxJJPJsrOz\nrT4gAAAA6FiW2KWnp/fr148xSId1NBrNq6++mpSUZPuh3Et1dfWGDRv69OljdR9YBv0Bih2B\n8eSUWPiKHgNFUYZ9YysqKqw+IAAAAOhY8I6dUql85ZVXrO65aYim6TfffLNXr14WvfLlXhQK\nRVZW1r179zIyMtLT069fv56enm44gYTVRCIRowHM7kpKShglU6ZM8fLysvqABQUFjJKqqiqr\njwYAAAA6FiR2a9asuXr1qmE5m802PbYtm80eOXJkcnKy4ZgaBQUFS5YsWb58uflhuIWampqB\nAwfevXs3JydHo9E47kSWdpuwlEqlMhyX+P79+/Y9i+EpAAAAwArmPopVq9Vr165lFPL5/CVL\nluTl5Zl+5Y7D4Rw8eLC4uPjMmTNdu3ZlbN2yZYsdW7BchEKhOH78+IMHDxya1RFCOBzr+zWb\nw/T4dvaCFjsAAAC7MDexO378eE5Ojn5JWFhYcnLy7NmzQ0JCzDoTi9WzZ88zZ8706dNHv7yk\npOSXX34xMwyPQVFURESEs6OoW/20paHFDgAAwC7MTexOnz7NKPn666+tmD1WIBCsWbOGoij9\nwvPnz1t6HDcVHBzcr1+/L7/88uHDhytWrHB2OHUTiUT1cBa02AEAANiFuQ/yGKPIxsbGjhgx\nwrpTdujQYcSIEfr9YS9cuGDdoVxcVFRUx44dW+vx9/d3dlCWMRpwaWmp210IAABAQ2BuYsd4\nDtu7d29bzhobG6uf2OXl5dlyNBckFAozMzPNfEjtyng8nlAolEql+oVFRUVI7AAAAFyQuY9i\nGWPX2Th2WrNmzUwc3ANwuVwPyOq0DAejKSwsdEokAAAAYJq5iR1jYlMej2fLWRntPZbOSwb1\nKSYmhlFy8+ZNp0QCAAAAppmb2AUFBemv2vjwlDHpAuPgDUR1dbWzQzBLXFwco8SwJw0AAAC4\nAnPfsQsJCdFP5k6cOPHpp59afdZz584xDm5RswwCAAAgAElEQVT1odxXVlaWs0MwS48ePRgl\np06dUqlU1g2hd//+/bNnz+qXNG/evGfPntbHBwAAAP8w99/m5s2bX7lyRbd65syZGzduGD6k\nM0dFRcWBAwf0S6Kjo604jrtzl3avbt26cblcpVKpKykqKtqzZ8/YsWOtONrbb7/NGLZw9erV\nSOwAAADswtxHsYMHD9ZfpWn6tddeUygUVpzy3XffZcxnwDh4Q5CWlsZotnRZIpEoISGBUbhs\n2TIrJtVISUkxHIy6Ad59AAAABzE3sRsyZAhjVOELFy4MHTrUojkDNBrNO++8s3XrVkb5sGHD\nzD+IB1AoFK+99pqzo7DA22+/zSi5du3aggULLD3OZ599xiiJiYlp1aqV9ZEBAACAHnMTu9DQ\n0P/7v/9jFP7++++tW7fetm0bo8+sIZqmT5w40bVr1y+//JKxaciQIeHh4WaG4QGUSuWECRNS\nUlIMN6lUqvqPxxxxcXGdO3dmFC5cuHD//v3mH+TLL780nFN49uzZtgYHAAAA/zA3sSOELF26\nlMvlMgrz8/MnTJgQEhIyfvz4devWJScn37p1Ky8vLz8//86dO3/99deWLVumT5/epEmT/v37\np6amMqqz2ezly5fbehHuIzs7e9CgQTt37jS69c6dO/Ucj/m++OILRpOtRqN54YUXli9fTtO0\n6bo0TS9btmzGjBmM8ubNm48ZM8bOgQIAADRktCXmzp1r37N/8MEHFgXgFg4dOuTr68sozM7O\nnjNnjpeXl4lPg6KoI0eOmDiyTCZjVGnSpIkVER47doxxnAkTJtRZ69133zUadrt27fbt21dT\nU2NYpaamZtOmTW3atDGsxWazz5w5Y/qM6enpjFr9+/e34noBAAAaCMtGrFiwYEF2dvb3339v\nUa3avPDCC0uXLrXLoVyNSqW6fPlydXV1UVFRamrquXPnzp07x+ht0LZt21u3btF6zV00TQ8f\nPrxv375t27aVyWRr164VCAT1HnutlixZ8vvvvxsmW9evX3/++ee9vLx69erVvHnzoKAgpVKZ\nk5Pz8OHD9PT02qYV+eSTT9AZFgAAwL4sS+woivruu+9YLNa2bdtsPPGYMWO2bdvGYlnwLNiN\nSCSS2NhYEzsMHTp0165do0aN+u233xibTp48efLkSULI6tWrHRii5QQCwfHjx5999tmrV68a\nbq2pqTG8ltq88847toyDCAAAAEZZnFdxOJytW7fu3r3b6mngfX19f/jhh507d9o4L5n7euut\nt5KSkkQi0axZs9wrtQ0NDU1OTu7Tp4/VR2CxWPPnz1+9ejXjjT0AAACwnZVZxQsvvHDv3r1l\ny5ZZNLZwZGTkokWL7t69O27cOOvO6+5iY2P/+OOPdevWsdlsQki/fv0Muwm7OB8fn99++23F\nihV+fn6W1o2Jifnzzz/nzZvniMAAAADA+uaigICAWbNm3b9//8SJE5999tmgQYMaNWrEaIah\nKCowMHDgwIHz5s377bffsrKy5syZ0wBnhmWxWD169NixY8eFCxd69eqlv+mtt976+eefDbsX\nBAUFuWxjHo/Hmzlz5r1792bMmGHmdHBxcXE//vhjampqt27dHB0eAABAg0XRdY1VYRGNRlNe\nXl5WVkbTdEBAgJ+fn8tmJ46TlJQ0cuTIpk2bNm7cOCIiom/fvs8995zpBEitVt+4cSM9Pb26\nujooKKh9+/bNmzd3aJDV1dWVlZUV/wgMDOzatasVx6Fp+tKlSz/99NPly5eLiooKCwuLior4\nfH5AQEBgYGC7du169OjRp0+fli1b2v0SAAAAgMHOiR0QQpKSkhITE8vLy50dCAAAADQsDa45\nDQAAAMBTIbEDAAAA8BCWjWMH5sBAHgBuQaPR5OfnW1ExJCSEw8EvTwBwRfjdZH/du3dfsWKF\ns6MAgDrIZLLz589bOqCmSqWKj48PCAhwUFQAALZAYmd/QUFBkyZNcnYUAA3LvXv3LO2DL5PJ\naJq2tFYD7OkPAG4EiR0AuD2aplNTU728vCyqpVAo1Gq1g0ICAHAK/OkJAAAA4CHQYgcAYAGa\npnNzc60YqLJJkyZ4jAsAjobEDgDAAmq1+s6dOwKBwKJacrk8NDRUKBQ6KCoAAC38+QgAAADg\nIdBiBwDgcCqV6vDhw2w229JaL730koNCAgCPhMQOAMDhNBqNQCDgcrkW1aqpqXFQPADgqZDY\nAYBr2blzp6XzOlAUpVQqLR3uBADA8yCxAwDXwmazrUjR5HK5I4IBAHAv6DwBAAAA4CHQYgcA\n4KKUSuXu3bstraXRaNDlAqDBQmIHAOCiaJr29va2tJZUKnVEMADgFvAoFgAAAMBDoMUOAACs\npFKp1Gq1pbU4HI6lQ/pZLTMz04pafn5+AQEBdg8GoB4gsQMAAJKTkyORSCytlZ+fX1JSYmmt\n0NDQRo0aWVrrypUrPB7P0lpyudzPz8/SWq1atUJiB24KiR0AABC5XH7//n1La5WXl1uRNuXk\n5FRUVFhaixBixTg4CoXCihMBuC8kdgAAHkWlUt26dcvSWo8fP3ZEMABQz5DYAQB4FI1GU29t\nbwDgapDYAQAA/EdBQYFKpbK0VlRUlBXD0wDYFxI7AACA/7DiLUCapr28vJDYgdNhHDsAAAAA\nD4EWOwBwlPv371MUZWktK8ZFAwAALSR2AOAoKSkpQqHQ0loajcYRwQAANAR4FAsAAADgIZDY\nAQAAAHgIJHYAAAAAHgKJHQAAAICHQOcJAAAAO1CpVHK53NJaPB7Pis7jALVBYgcAAGArmqbP\nnz+flpZmUS21Wt2nT5+QkBAHRQUNEBI7AAAAO+BwOHw+36IqarWapmkHxQMNExI7AAAA59Bo\nNCdOnODxeJbWGj58uBWDREJDgMQOAADAOWia5nA4Xl5eFtVSKBRKpdJBIYG7Q2IHAAAA7io7\nO9uKeQg5HE5UVJQj4nE6JHYAUDeVSoUpXAEaoOrq6qKiIktrcbncyMhIS2tJpVKpVGpprcrK\nyuzsbEtrtWrVytIq7gKJHQDUbd++fVwu19JaCoUCrwEBuLX8/PyMjAxLa0mlUisSu+zs7Nu3\nb1taq7q62t/f39JaHgyJHQDUjcViWdrdjxAik8kcEQwAWKGqqqqoqMjSMfMKCgocFI8hDodj\nxR+QGAWQAYkdAACAO1Gr1T///DObzbaolkKh8PX1ZbEsm3GqvLzcz8/Poirac+3du9fSWjKZ\nDG1vtkNiBwAA4E5omubz+S4+Zp6lXX0JIVbM2wGGMFcsAAAAgIdAYgcAAADgIZDYAQAAAHgI\nJHYAAAAAHgKdJwAalszMzJSUFEtrYf4iAAC3gMQOoGFRq9VW9FZTKBSOCAYAAOwLj2IBAAAA\nPARa7AAAAKBhOX/+/JUrVyyt1a5du7Zt2zoiHjtCYgcAAAANC5vN9vb2trSWFTOe1T88igUA\nAADwEEjsAAAAADwEEjsAAAAAD4F37ADclUKhkMlkltbCiHQAAB4MiR2Au7p3796tW7csrSWV\nSv39/R0RDwAAOB0SOwB3xeVy+Xy+pbVqamocEQwAALgCvGMHAAAA4CGQ2AEAAAB4CDyKBXC+\nO3fupKamWlpLLpcHBAQ4Ih4AAHBTSOwA7Ekul1dVVVlaSyqVWjEGOvq3AgAAAxI7AHvKycm5\nceOGpbWqqqrQUxUAAGyHxA7Azjgci79WFEU5IhIAALAjtVotl8strcXlclms+uvSgMQOwLjC\nwsLTp09bWgvvvQEAeKpLly5Z8UzmySefbNWqlSPiMQqJHYBxUqlUKBRa2paG994AADwVi8Wy\nYvTQ+myuI0jsoCEoKys7fvy4pSmaTCbz9fXFQ1IAAHAjSOzAnchksrt371r6EltZWRmXy+Vy\nuRbVUigUFu0PAADgdEjswDk0Gs29e/csTdGqq6vv3LkjEAgsqiWRSCzN6gAAANwREjs3UFNT\nY8WbW1aMpkYIKS0ttWJAtT///NPS1w40Go1GoxGLxRbVksvlGo3GoioAAABOVFRUxGazLa0V\nEBDg5+dnxekomqatqAYmyGSyX375Ra1WG26Sy+VW9JSmadqKVy/VarUVP0kajcaKWtady4pa\n2qzO0k9DrVZTFGVFLRaLZek7dvX2UaCW7bWs+3HS/s609AdDo9FQFGVFrXr77qOWs2pZcZet\n/k2I32luVIvH4/F4vNq2xsfHBwUFGd2ExM7+0tLSBg0aZPQNrRYtWnh5eVl6wICAgNLS0vqp\n1ahRo+Li4nqoxWazfXx8ysrKLKrl5eXFZrOrq6stquXr6yuTyRgptUAg8PLykkgktb1LFxgY\nWF5ebjRBN8G6D9C6mxUYGFhSUuJ556rtMxSJRFwut6KiwrDVlqKogIAASyPk8XgCgaCystKi\nWiKRSKPRSKVSi2r5+flJJBJLm96t+3Gy7mY54hb7+flpNBrDT9gVfpxMsyJCNpvt6+traYR8\nPp/H41n6gEUsFiuVSplMZlGtgICAiooKE7/TtL+W5XK5/o+36/w42beW6//+LCwsLCoqMrqJ\nxWKtX7/+xRdfNLoViR00UDt37ly5cuWiRYsGDRrk7FjALLNmzTp58uQvv/wSHBzs7FjALAMG\nDPD29j506JCzAwGzZGVljR49esSIEZ9++qmzYwHr1evYKgAAAADgOEjsAAAAADwEesVCAyUS\nicLDw6145RGcJTAwMDw83IqpeMFZwsLC8BVzI1wuNzw83LqemOA68I4dAAAAgIfAo1gAAAAA\nD4HEDgAAAMBDILEDAAAA8BB4DRkalry8vLfeekuj0UyePHn48OFGdzhy5MjVq1eLi4sFAkFo\naGjv3r0HDx5sYgRwcCjcEbeAb5a7ePz48Z49e7KysnJzc4VCYWRkZPv27UeMGGE4oTZumZtC\nYgcNiFQq/eqrr0zMNnvlypWlS5fqxnNXKBSVlZV37txJTk5esGCBFbPogo1wR9wCvlnu4vTp\n01999ZXuRkil0uLi4tTU1F9//XX27NnNmjXT7Ylb5r7wKBY8HE3TVVVV2dnZBw8efPfdd69f\nv17bnuXl5StXrtT9IvP39xcIBNrlu3fvfvPNN/URLujBHXFl+Ga5ncePH69fv153I0Qika7t\nraCgYPny5br5FXHL3Bpa7MDDXbx4cdGiRebsefToUe2MjSwWa+7cuV27dqVpes2aNSdPniSE\nJCcnv/zyy5jMqj7hjrgyfLPczs6dO7VTZnM4nNmzZz/11FNqtXrnzp179uwhhOTl5f3yyy8j\nR44kuGVuDi12AH87f/68dqFLly5du3YlhFAUNWHCBG0hTdMXLlxwVmwNE+6IZ8B9dBGZmZna\nhb59+z711FOEEDabPW7cuLCwMG353bt3tQu4ZW4NLXbg4Z544onZs2drl2Uy2erVq43uplAo\nsrOztcsxMTG6cj8/v6ioqJycHELInTt3HBws/At3xMXhm+VeaJrOzc3VLj/xxBO6coqimjRp\nkp+fTwjR7oBb5u6Q2IGHCwgIiIuL0y5LpdLadqusrNTNwhISEqK/KTg4WPu7rLy83GFhAhPu\niIvDN8u90DQ9YsQI7XKrVq30N0kkEu2CtksEbpm7Q2IHQAgh1dXVumXG7Ja6Vd2vP6gHuCOe\nAffRRbBYrPHjxxuWFxUV3bp1S7vcsmVLglvm/vCOHQAh//1dxufz9Tfpfpfp7wOOhjviGXAf\nXVl1dfUXX3yhVCoJIQKBYNiwYQS3zP2hxQ48QWFhoe6PTp0WLVpERESYeQTdowdDFEVpF1Qq\nlXXhgRVwRzwD7qPLys/PX7x4sfZ1Ojab/eGHHzZq1Ijglrk/JHbgCW7duvW///2PUTh58mTz\nEzuRSKRbrqmp0d+kW2U8lQCHwh3xDLiPrunMmTPr1q3T3gKRSPT+++937txZuwm3zN0hsQMg\nhBCxWKxb1g3LqaX7Xab/+w4cDXfEM+A+uhqVSrVp06ajR49qV6Ojoz/++OPQ0FDdDrhl7g6J\nHQAhhPj4+FAUpX0GUVBQoL/p0aNH2oXIyEgnRNZQ4Y54BtxHl1JVVfXZZ5/pBiuJj49/6623\nGC/S4Za5OyR24Ani4+Pj4+NtOQKPx4uOjn7w4AEh5OrVq88//7y2vLS0NC8vT7vcokULm6IE\nS+COeAbcR9ehVquXLl2qy+omT548fPhww91wy9wdesUC/E03KNe1a9d+/vlnmUxWVFS0atUq\nbSGLxerevbvzomuIcEc8A+6jizh27JhuSt8ePXrExcWV/FdFRYV2K26ZW6NM9H8B8DBSqXTM\nmDHaZcO/VisqKqZOnaqdIdHQ0KFD33jjDYeHCHpwR9wFvlluYfr06bopJYxq0aKFthcabplb\nQ4sdwN98fX1nzpwpEAgMN7Vu3dro2J7gULgjngH30RVIpVLTWZ0+3DK3hnfsAP7VuXPnVatW\nHT58ODU1tbS01MfHJzo6umPHjkOGDOFyuc6OriHCHfEMuI9Op50N1ny4Ze4Lj2IBAAAAPAQe\nxQIAAAB4CCR2AAAAAB4CiR0AAACAh0BiBwAAAOAhkNgBAAAAeAgkdgAAAAAeAokdQH3Izs6m\n/svoLI0A4Mrc6ItcVFS0bdu2MWPGxMbGRkVFCQQCX1/fZs2axcfHz5kz59dff9VoNM6OERwC\niR00aMnJyVTtrl69aqLupUuXTNTdv39/vV2FI6xfv97E1ZkvLCzM2ZcC0LDcv39/9OjRoaGh\nEyZM2L179+XLlx8+fCiXyysrK7OyspKTk5csWTJ48OAWLVqsXr1apVI5O16wMyR2ALU6d+6c\nia3nz5+vt0gAAMwxf/78tm3b7tu3r87ZB7KysmbMmPHUU0+lp6fXT2xQP5DYAdQKiR0AIeSD\nDz5gNMT++uuvzg4KmFQq1YQJEz777DOFQmF+rdTU1N69e6empjouMKhnSOwAaoXEDgDcxeTJ\nk7dt22ZFxbKysgEDBuTl5dk9JHAKjrMDAHBdOTk5ubm5ERERhpvy8/Ozs7PNPxSXy23durV+\nidHDAoArc9kv8t69e7du3WpYTlFUXFxc//79IyIi5HJ5ZmbmH3/8cfnyZcZuJSUlU6dOTUpK\nqo9YwcGQ2AGYcu7cuRdffNGw3NLmusaNG9+6dctOQdWH559/vmvXrkY3paenT5o0iVGYnJzM\n5/MNd+ZyufYPDsBJXPOLXFZW9uabbxqWx8bGfvvtt+3atWOUHzp0aOrUqfn5+fqFhw8fPn36\ndHx8vOPihPqBxA6Aic1mq9Vq7bKZiZ1+Fav9+eefW7Zs0S8ZPXr0wIEDCSHXr1/fsmXLiRMn\n8vLyqqurGzVq1LFjx+HDh0+YMMFoOmW7kJCQkJAQ8/fv1q2b+ZHIZLIjR4789NNPKSkphYWF\nVVVVwcHB4eHh/fv3T0hI6NSpU20Vjxw5cvjwYf2Sd999NyYmRqFQbN26ddeuXbdv3y4tLQ0N\nDW3VqtW4ceNeeOEFgUCg2zktLW3r1q3Hjx9/9OiRVCoNCgrq2rXrqFGjxowZw+EY+WX4ww8/\nJCcn65d88MEHLVu2VCqV+/bt27JlS0ZGRmFhYXBwcNOmTRMSEl566aXg4GDHXb4JFRUVK1as\n+PXXX2/evLl69WrDzPvy5cvaDpJ3796tqKhQqVTh4eERERGRkZExMTEvvvhi06ZNrThv/avz\nSh3x8Vqk/r/ImzdvLi0tZRT269fv559/NnrYkSNHhoaG9unTh/E23pYtW5DYeQIaoAE7ffq0\n4ZeiS5cuuuXOnTsbrdijRw/dPtHR0Y0aNWIcRNsrTeevv/5i7DBhwgTGMQ2fpKxYsUKlUn3y\nyScslvHXYaOjo1NSUhz16dTC8FoIITKZzMzqe/bsiY6Oru03EiEkISEhOzvbaN358+czdj52\n7NitW7c6dOhg9FAxMTG3bt2iaVqhUMyaNau2j7F9+/b37t0zPJ1hK8jp06fz8/O7d+9u9Dg+\nPj6bN2923OUb/Qmhafrs2bOBgYG6wq+//lq/VkZGRq9evUyckRBCUdTEiRNLSkqMnvf9999n\n7H/s2DEzPy6jB/zpp58Ye86ePdv2K7Xx4zWHC36RVSqV4SWHhoZWVFSYrrh48WJGLW9vb6VS\naV0Y4DrQeQKAqWfPnrrltLS06upqxg4KhSIlJUW3qp/k2RdN02+88cbChQtrG0o0Ozs7Pj7+\n7t27DgrA7j799NMXXnjB9OuJBw4c6Ny5s5nd9G7duhUfH5+WlmZ0640bN/r3719cXDxhwoQv\nvviito/x2rVrAwcOLCsrq/N0Eomkb9++tT2Ir6ysnDRp0uzZs2urbvfLJ4SkpKQMGTKkpKTE\n6NYrV67ExsaeOXPG9EFomt6yZUvfvn31PwTdWIYrVqxg7D948GDtpo8++sjMOG1n+kqJYz5e\nu3DoFzk5OdnwkhcvXuzj42O64sSJE9lstn6JRCK5d++eFTGAS0FiB8Ckn6ip1eoLFy4wdkhN\nTZXJZLrVuLg4B0WyZcuWb7/91vQ+VVVVr7/+uoMCsK9ly5YtXLjQnD1LSkr69etnzr9z77//\nfmFhoYkd8vLy2rVrt2PHDtPHyczMXLJkSZ2nmzNnTp2vWC1btmzDhg1Gy+1++Wq1+tVXX62s\nrDS6VSKRjBgxorathtLS0kxkpc5l+kqJYz5ee3HoF9mw876vr+9LL71UZ8XQ0NBnn31W8F+3\nb9+2IgZwKUjsAJj0W+yIsd+bjAYbx7XY3bx5U3+Voiiju50+fdr0JBmu4MaNG/PmzWMURkVF\njR8/ftasWf3792c0HtT2PjiD/quNtX0+BQUF+qu17bZ58+Y6J1nSbxrk8/m+vr5Gd5s9ezYj\n3XTQ5f/www/Xrl2rbeuqVasMx7Dw8/Pr1KlTfHx869atDT+Kb7/91pyWy/pn+kod9PHai0O/\nyH/++SejZPjw4fqvlppw5MiRmv8aOXKkpQGAq0FiB8AUFhbWrFkz3arpxE4sFht2OrOvnj17\n/vbbb0VFRRKJ5NKlS9q3sBmOHz/u0BhsN3fuXLlcrl+SkJBw8+bNbdu2LVu27Pjx47///juj\n58HJkycPHDhQ55G9vLxWr16dmZkpl8uvXLnSv39/63YrKyu7ceOGOdfSvHnzo0ePSqXS8vLy\njIyM559/nrFDZWXl0qVL9UscdPnXr183sfXHH39klCxatCg/P//KlSunTp26devWjRs3mjRp\nor+DWq0+deqUdtnb2zsiIiIiIkIkEjGO06hRI+2mOp/32YvpK3XcT5cdOeiLbDh2Sbdu3awM\nETyDk9/xA3Aqo50naJoeP368btXHx0etVuvXioyM1G0dMGAATdMO6jxBCJk0aZJGo9HfTaPR\nGA5EMm7cOId8QMZY0Xni0aNHjG6nERERhlUMJ9gdNGiQ/g6GnScIISdOnNDfRyKRGH193pzd\nGH0CjDbqREdHFxUVMSKfMGECY7egoCDde+j2unyjPyFavXr1mjp16vLlyz/55JOzZ8/SNJ2b\nm2v6aFqGQ9quXr2asY8rdJ4wcaX2+njN4WpfZLVabdj+V9vHDg0EWuwAjNB/ulpZWanfWpCX\nl/fw4UOje9pdaGjo//73P8Yvboqi3nvvPcaeJt4odwX79u1jzDX+4YcfGg7EkJCQoN8lmRCi\nHZrExJEHDhzYt29f/RKhUDho0CDrdjPsKGNo4cKFQUFBjMKVK1cyHn49fvxY1/TluMsnhERE\nRPz2229//PHH+vXr33///QULFmh/Jg3fIRs6dKhhdcPxWcrLy02f0Vlqu1KHfrx24bgvsrbr\nK6PQnDF3wIMhsQMwwsRrdvX2gh0hJD4+XiwWG5a3adOGUaLfmcMFGXZAMUyqtBhPSDUajeEr\nRPqMDuQRFhZm3W51atSo0QsvvGBYHhAQYDjeoe4ZmeMunxDyzTffDBgwwLC8U6dOV/8rMTHR\ncDfT8+a5lNqu1KEfr1047otsOHwdIaTeno+Da0JiB2BEmzZtAgICdKu1JXZsNtuhr7O0bdvW\naLlFQwe7AsZrQD4+Pi1atDC6p+H4sfojyxgyOqyu4cMpM3erU0JCQm0DyRr2Q7xy5Yp2wXGX\nHxsbO2TIEKObfH19O/yX/r/3paWlv//++4cffmhOX2BXYOJKHffx2ovjvsiGzXWEkNpGy4MG\nAjNPABhBUVSPHj2OHDmiXdVP7PT/xG/fvr3RP8TtRSgUGi13u1/cjPe9ampq9Lun6DNssTDd\nocHM3n9m7lYnE9MzMHohEEJ0PVIdd/nmd9zRtk6dPXs2JSUlJSUlKyvLzIouwsSVOu7jtRfH\nfZH1//7UqaqqsqI1GjwGEjsA4/QTu+zs7Ly8vPDwcLlcrj+0qUOfw3oMpVIpkUgYJQ8ePDCz\nukuNvmFixnfDTdqX1Rx6+ebMA3b16tUNGzYkJSWZHvDPxdV2pZ7002UFPz8/iqIY7XbuflFg\nIzf7ux+g3hh9ze7KlSv6oyogsTOH+QPkGlVRUWGvSGxn4rV0b29vxrAgVVVVxMGX7+XlZbr6\nvHnzunTpsnHjRrfO6kjtV+pJP11WYLFYho12jGHzoKFBYgdgXNeuXfXfptImdvXZc8Jj2DK7\nOSGkpqbGXpHY7vHjx7VtksvljKYj7WN6J17+4sWLFyxYUNuoy76+vq+88sqCBQusPr4r8KSf\nLut07tyZUWL+hGkqlUr+X4z+xeCOkNgBGMfn82NjY3WrholdRESE/oB2UBuRSMQY979jx47m\nj8nkUjPhGg4Op5OTk8N4Iubv70+cd/l5eXmGw/55e3snJCRs2LAhNTW1uLh4+/btffr0se74\nLsKTfrqsYzil4eHDh412qjA0evRoxpRia9ascUCMUK+Q2AHUSr9BLi0tTSKR6Cd2aK4zX2Bg\noP6q2725r2MicsNN4eHh2gWnXP7u3buVSqV+Sc+ePe/du7d///4333yzY8eO2kF9c3JyHBdD\nbemFVCq141k85qfLOoa/iLKzs3VjKJqg0WiSk5MZhbV1KAY3gsQOoFb6r9mpVKq9e/fqz7yJ\nxM58HTp00F+tqKhw2VFwTTt48KBCoShb1hYAAAZZSURBVDC6adeuXYySp556SrvglMvPyMhg\nlKxbty40NJRRaH4/AysUFRUZLbfvS2Ae89NlnWeeecaw484HH3xQ58TH58+fN+xmERMTY8/g\nwBmQ2AHUKi4uTn+os5UrV+pvdeXETqFQlPyX0YFM643haH+GTQVaGo2m4r9c6i2ooqKivXv3\nGpYXFxfv3LmTUdi9e3ftglMuPzMzk1HyxBNPMEpomjZ6OXVSq9WGhYZjyty+fdtwN5qmjx49\nasVJa+MxP13W4XA4hpO5XblyZebMmSZqyeVyw3kv2rdvjxY7D4DEDqBWAQEB+iOLpqen65ZF\nIhGjncClHDx4sNF/OfcPccPJrJYuXWp0z2+++cbvv7Zs2eL4AC3w6aefGmbJM2bMYIyRFh0d\nrUvsnHL5hq1lhqnegQMHrl27ZsXBi4uLDQsNu2d+8803hpnT6tWrDeeKsIUn/XRZZ/LkyYaz\nTaxevfq1114z2u33+vXrgwcPvnjxIqN83LhxjgoR6hESOwBTamuW69atG+OVbTDh6aef7tix\no37JX3/99dFHHzG64J08eXLu3Ln6JUKh0HCqLufKzMzs0aOH7h2m+/fvjxo16ocffmDs9tpr\nr+mGn3XK5TPePCOEvPPOO7qpSNVq9aZNm8z8h9zwUOvWrbt48eKdO3f0e5MYjiH86NGjPn36\nnD9/vqamRiKRXLx4cezYsYYNRTbypJ8u6wQHB69bt86w/LvvvmvatOnrr7++devWY8eO7dq1\na8mSJQkJCR06dDh9+jRj5+jo6GnTptVHuOBgGKAYwJSePXtu3LjRsNyVn8O6pvnz548cOVK/\nZOnSpUeOHOnZs2dkZGRpaenZs2cNmxDmzJljmFU43e3bt/v27SsSiQQCgdG2q/Dw8HfffVe/\npP4vv0uXLow36E+ePBkdHd2uXTuKotLS0szvwdCqVStGyaVLl7QPQGfPnq2blKx37958Pl9/\noEftnrpXGszsqmkFT/rpss4rr7zy888/7969m1FeVla2adOmTZs2ma7OYrG++uqr2mbIAPeC\nxA7AFMYwxTpI7Cz13HPPJSYmbtu2Tb/wxo0bJuZ0euaZZz744APHh2YBsVisHXaYEFJdXV1d\nXW24D0VRX331FWOuufq//LFjx65cuZKRS0kkkr/++ku/hMfjMbqDGL5QHxcX5+fnV2ePhICA\ngHHjxn333XeGmxhhiEQiox+d1Tzjp8tG27dv12g0Vrw0SVHU5s2ba5uKF9wOHsUCmNK0adPG\njRszClks1tNPP+2UeNzaN998k5CQYObOAwcOPHjwII/Hc2hIllq+fLm3t7fpfdavXz9ixAjD\n8nq+/E6dOk2ePNn0PtHR0bp583QYo3ATQkJCQtavX2/OxKZffPFFnYM7PvHEE44YLM0Dfrps\nxOPxdu3a9d5771n0lkhISMjBgwcnTpzouMCgniGxA6iDYaNdu3btDF9Vhjrx+fy9e/cuXbrU\n19fXxG5BQUFffvnl0aNHTe/mFK1btz58+HBtE4uFhoYePHhwypQpRrfW/+WvX///7d2/62lx\nHMdxicWi71Gkkx+TkUUpC6UQg0USJWUy+xdkU5ZbksEqGZSSjTIdC8MxmLAhMpO6w13kXtfN\n7et2P9/nYzznffr86DO8+nzqfL4VCoVHbzOZjKIowWDwbnNRUZRKpfJzsaqq+Xze4/FIkqTT\n6YxGoyzLd0eZJpNpPB673e5HjUaj0fF4bLFYXhrQ7wiwuv6eVqutVquz2SwajT4tliSpVCqp\nqppIJN7QN7zN/eXBAPDZjsdjp9MZDAaqqu52u8vlYrfbHQ6H0+kMh8OxWOzpFajvUSwW6/X6\n7ZPRaBQIBHa7XbPZ7PV6q9XqdDqZzWaXy5VKpdLp9J/EhTcPfzKZtFqtxWKxXC7P57PNZguF\nQrlczuv1/ihoNBp3u3SyLJfL5ZdbvF6v7Xa72+1Op9P9fq/X661Wq8/ny2azkUhEo9HM5/Na\nrXb7STweTyaTL7d4639ZXZ9ts9n0+/3hcLher7fb7eFwMBgMHx8fsiz7fD6/3/91puKrIdgB\nwK89Cnb/qj8A8BRHsQAAAIIg2AEAAAiCYAcAACAIgh0AAIAgCHYAAACCINgBAAAIgmAHAAAg\nCIIdAACAIPhBMQAAgCDYsQMAABAEwQ4AAEAQBDsAAABBfAeFVNt5L80n0wAAAABJRU5ErkJg\ngg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt <- read.csv(file=\"model_data/Fig2B_binned_tmin_sleep_offset_model_output.csv\",header=T)\n",
    "flex.plot <- binned.plot(felm.est = dt,\n",
    "                 plotvar = \"CUTTMIN\",\n",
    "                 breaks = 5,\n",
    "                 omit = c(5,10), \n",
    "                 ylimit = c(-4,7.6),        \n",
    "                 linecolor = \"#87CEEB\",\n",
    "                 pointfill = \"#87CEEB\",\n",
    "                 errorfill = \"#87CEEB\",\n",
    "                 xlabel = \"Min. Temperature in C\",\n",
    "                 ylabel = \"Change in Offset Timing (Minutes)\"\n",
    "                 )\n",
    "# hist <- axis_canvas(flex.plot, axis = \"x\") + \n",
    "#         geom_histogram(data = Climate_Controls_DF, aes(x = Climate_Controls_DF$tmin), bins=41, fill='gray60', color='gray60', alpha=0.75, size=0.1)\n",
    "# flex.plot <- insert_xaxis_grob(flex.plot, hist, position = \"bottom\", height = grid::unit(0.1, \"null\"))\n",
    "flex.plot <- ggdraw(flex.plot)\n",
    "#ggsave(\"flexplot_offset.pdf\")\n",
    "flex.plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{Binned Nighttime Minimum Temperature Sleep Onset, Midsleep and Offset Regressions} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{1}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-2} \n",
      " & Change in Sleep Offset Time \\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " CUTTMIN(-Inf,-15] & 2.972$^{***}$ \\\\ \n",
      "  & (1.017) \\\\ \n",
      "  CUTTMIN(-15,-10] & 0.369 \\\\ \n",
      "  & (0.724) \\\\ \n",
      "  CUTTMIN(-10,-5] & $-$1.310$^{**}$ \\\\ \n",
      "  & (0.525) \\\\ \n",
      "  CUTTMIN(-5,0] & $-$1.019$^{**}$ \\\\ \n",
      "  & (0.512) \\\\ \n",
      "  CUTTMIN(0,5] & $-$0.475 \\\\ \n",
      "  & (0.296) \\\\ \n",
      "  CUTTMIN(10,15] & $-$0.002 \\\\ \n",
      "  & (0.255) \\\\ \n",
      "  CUTTMIN(15,20] & $-$1.160$^{***}$ \\\\ \n",
      "  & (0.433) \\\\ \n",
      "  CUTTMIN(20,25] & $-$2.117$^{***}$ \\\\ \n",
      "  & (0.542) \\\\ \n",
      "  CUTTMIN(25, Inf] & $-$2.511$^{***}$ \\\\ \n",
      "  & (0.727) \\\\ \n",
      "  tmin\\_norm\\_w7 & 0.068 \\\\ \n",
      "  & (0.124) \\\\ \n",
      "  CUTPRCP(0,1] & 0.922$^{***}$ \\\\ \n",
      "  & (0.155) \\\\ \n",
      "  CUTPRCP(1,2] & 1.973$^{***}$ \\\\ \n",
      "  & (0.386) \\\\ \n",
      "  CUTPRCP(2,3] & 2.689$^{***}$ \\\\ \n",
      "  & (0.388) \\\\ \n",
      "  CUTPRCP(3, Inf] & 1.840$^{***}$ \\\\ \n",
      "  & (0.613) \\\\ \n",
      "  prcp\\_norm\\_w7 & 6.427$^{***}$ \\\\ \n",
      "  & (1.322) \\\\ \n",
      "  CUTTRANGE(-Inf,0] & 0.381 \\\\ \n",
      "  & (1.542) \\\\ \n",
      "  CUTTRANGE(0,5] & 0.432$^{*}$ \\\\ \n",
      "  & (0.230) \\\\ \n",
      "  CUTTRANGE(10,15] & $-$0.532$^{**}$ \\\\ \n",
      "  & (0.217) \\\\ \n",
      "  CUTTRANGE(15,20] & $-$1.977$^{***}$ \\\\ \n",
      "  & (0.380) \\\\ \n",
      "  CUTTRANGE(20, Inf] & $-$2.728$^{***}$ \\\\ \n",
      "  & (0.840) \\\\ \n",
      "  trange\\_norm\\_w7 & $-$0.023 \\\\ \n",
      "  & (0.184) \\\\ \n",
      "  CUTCLOUD\\_rean2(0,20] & 0.341 \\\\ \n",
      "  & (0.629) \\\\ \n",
      "  CUTCLOUD\\_rean2(20,40] & $-$0.082 \\\\ \n",
      "  & (0.653) \\\\ \n",
      "  CUTCLOUD\\_rean2(40,60] & 0.081 \\\\ \n",
      "  & (0.673) \\\\ \n",
      "  CUTCLOUD\\_rean2(60,80] & 0.644 \\\\ \n",
      "  & (0.692) \\\\ \n",
      "  CUTCLOUD\\_rean2(80,100] & 1.782$^{***}$ \\\\ \n",
      "  & (0.635) \\\\ \n",
      "  cloud\\_norm\\_7\\_rean2 & 0.053 \\\\ \n",
      "  & (0.034) \\\\ \n",
      "  CUTRHUM\\_rean2(0,20] & $-$2.360 \\\\ \n",
      "  & (2.097) \\\\ \n",
      "  CUTRHUM\\_rean2(20,40] & $-$0.222 \\\\ \n",
      "  & (0.938) \\\\ \n",
      "  CUTRHUM\\_rean2(40,60] & 0.164 \\\\ \n",
      "  & (0.320) \\\\ \n",
      "  CUTRHUM\\_rean2(80,100] & $-$0.047 \\\\ \n",
      "  & (0.224) \\\\ \n",
      "  rhum\\_norm\\_7\\_rean2 & $-$0.147$^{***}$ \\\\ \n",
      "  & (0.055) \\\\ \n",
      "  CUTWIND\\_rean2(5,10] & 1.246$^{***}$ \\\\ \n",
      "  & (0.157) \\\\ \n",
      "  CUTWIND\\_rean2(10,Inf] & 2.323$^{***}$ \\\\ \n",
      "  & (0.294) \\\\ \n",
      "  wind\\_norm\\_7\\_rean2 & $-$0.235 \\\\ \n",
      "  & (0.189) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes \\\\ \n",
      "Date FE & Yes \\\\ \n",
      "State:Month FE & Yes \\\\ \n",
      "Observations & 4,405,171 \\\\ \n",
      "R$^{2}$ & 0.482 \\\\ \n",
      "Adjusted R$^{2}$ & 0.475 \\\\ \n",
      "Residual Std. Error & 75.809 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{1}{r}{Standard errors are in parentheses and are clustered on the first admin level} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# invisible(stargazer(flex_t_offset,\n",
    "#           type='latex',\n",
    "#           title = \"Binned Nighttime Minimum Temperature Sleep Onset, Midsleep and Offset Regressions\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Change in Sleep Offset Time'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Timing (Minutes)',\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the first admin level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#FIG 2C - Temperature vs. Sleep Midsleep FE Reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = midsleep ~ CUTTMIN + tmin_norm_w7 + CUTPRCP +      prcp_norm_w7 + CUTTRANGE + trange_norm_w7 + CUTCLOUD_rean2 +      cloud_norm_7_rean2 + CUTRHUM_rean2 + rhum_norm_7_rean2 +      CUTWIND_rean2 + wind_norm_7_rean2 | userID + unique_day_no +      stateyrmn | 0 | state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "     Min       1Q   Median       3Q      Max \n",
       "-1836.00   -34.54    -2.26    32.28   642.30 \n",
       "\n",
       "Coefficients:\n",
       "                       Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "CUTTMIN(-Inf,-15]       1.08380      0.84331   1.285 0.198732    \n",
       "CUTTMIN(-15,-10]       -0.79411      0.62253  -1.276 0.202086    \n",
       "CUTTMIN(-10,-5]        -1.82435      0.42152  -4.328 1.50e-05 ***\n",
       "CUTTMIN(-5,0]          -1.30974      0.39033  -3.356 0.000792 ***\n",
       "CUTTMIN(0,5]           -0.85120      0.24116  -3.530 0.000416 ***\n",
       "CUTTMIN(10,15]          0.95009      0.24629   3.858 0.000115 ***\n",
       "CUTTMIN(15,20]          1.06052      0.38794   2.734 0.006262 ** \n",
       "CUTTMIN(20,25]          0.98348      0.45505   2.161 0.030675 *  \n",
       "CUTTMIN(25, Inf]        1.00475      0.59224   1.697 0.089785 .  \n",
       "tmin_norm_w7            0.16074      0.10925   1.471 0.141197    \n",
       "CUTPRCP(0,1]            0.76754      0.13673   5.614 1.98e-08 ***\n",
       "CUTPRCP(1,2]            1.37521      0.29435   4.672 2.98e-06 ***\n",
       "CUTPRCP(2,3]            1.94729      0.31906   6.103 1.04e-09 ***\n",
       "CUTPRCP(3, Inf]         0.87609      0.54327   1.613 0.106824    \n",
       "prcp_norm_w7            4.96256      1.22846   4.040 5.35e-05 ***\n",
       "CUTTRANGE(-Inf,0]      -1.50994      1.41461  -1.067 0.285797    \n",
       "CUTTRANGE(0,5]          0.32754      0.17318   1.891 0.058589 .  \n",
       "CUTTRANGE(10,15]       -0.22181      0.18769  -1.182 0.237302    \n",
       "CUTTRANGE(15,20]       -0.88785      0.30124  -2.947 0.003205 ** \n",
       "CUTTRANGE(20, Inf]     -1.72358      0.75490  -2.283 0.022420 *  \n",
       "trange_norm_w7          0.02444      0.15601   0.157 0.875492    \n",
       "CUTCLOUD_rean2(0,20]   -0.08682      0.51317  -0.169 0.865653    \n",
       "CUTCLOUD_rean2(20,40]  -0.49537      0.54403  -0.911 0.362529    \n",
       "CUTCLOUD_rean2(40,60]  -0.47251      0.55880  -0.846 0.397782    \n",
       "CUTCLOUD_rean2(60,80]  -0.07352      0.56642  -0.130 0.896727    \n",
       "CUTCLOUD_rean2(80,100]  0.74180      0.53756   1.380 0.167600    \n",
       "cloud_norm_7_rean2      0.07730      0.02788   2.773 0.005561 ** \n",
       "CUTRHUM_rean2(0,20]     0.01856      2.29120   0.008 0.993536    \n",
       "CUTRHUM_rean2(20,40]    0.59927      0.79194   0.757 0.449221    \n",
       "CUTRHUM_rean2(40,60]    0.22670      0.27308   0.830 0.406445    \n",
       "CUTRHUM_rean2(80,100]   0.15181      0.18990   0.799 0.424031    \n",
       "rhum_norm_7_rean2      -0.20696      0.05679  -3.644 0.000268 ***\n",
       "CUTWIND_rean2(5,10]     0.97971      0.13114   7.470 7.99e-14 ***\n",
       "CUTWIND_rean2(10,Inf]   1.93513      0.26112   7.411 1.26e-13 ***\n",
       "wind_norm_7_rean2      -0.19715      0.17020  -1.158 0.246714    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 67.08 on 4345329 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.5075   Adjusted R-squared: 0.5007 \n",
       "Multiple R-squared(proj model): 0.0002715   Adjusted R-squared: -0.0135 \n",
       "F-statistic(full model, *iid*):74.82 on 59841 and 4345329 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 23.14 on 35 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# flex_t_midsleep <- felm(formula = midsleep ~ CUTTMIN + tmin_norm_w7 + \n",
    "#                                           CUTPRCP + prcp_norm_w7 + \n",
    "#                                           CUTTRANGE + trange_norm_w7 + \n",
    "#                                           CUTCLOUD_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           CUTRHUM_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           CUTWIND_rean2 + wind_norm_7_rean2\n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(flex_t_midsleep)\n",
    "\n",
    "# #save model coefficients for plotting\n",
    "# dt <- as.data.frame(summary(flex_t_midsleep)$coefficients[, 1:4]) # extract from felm summary (use df to keep coefficient\n",
    "# write.csv(dt,file=\"model_data/Fig2C_binned_tmin_sleep_midsleep_model_output.csv\")#save data as CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YOSk5OTkpLatGkTGFhzkzyr1Zqfn//9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\n3MYFFgb3pEA1tPxW27EgqimeBoJXQO8DdvvwBx8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mretzgDMA0bgnNQKsptPXzJwZ+uGHHhRKe/7O2Z3AjxEAACAASURBVOE2ULkZrJYD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\nAAAwyN/6Hl9RBBNiBwAwEfvClQRnlN4RAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt <- read.csv(file=\"model_data/Fig2C_binned_tmin_sleep_midsleep_model_output.csv\",header=T)\n",
    "flex.plot <- binned.plot(felm.est = dt,\n",
    "                 plotvar = \"CUTTMIN\",\n",
    "                 breaks = 5,\n",
    "                 omit = c(5,10),\n",
    "                 ylimit = c(-4,7.6),\n",
    "                 linecolor = \"blue4\",\n",
    "                 pointfill = \"blue4\",\n",
    "                 errorfill = \"blue4\",\n",
    "                 xlabel = \"Min. Temperature in C\",\n",
    "                 ylabel = \"Change in Sleep Midsleep Time (Minutes)\"\n",
    "                 )\n",
    "# hist <- axis_canvas(flex.plot, axis = \"x\") + \n",
    "#         geom_histogram(data = Climate_Controls_DF, aes(x = Climate_Controls_DF$tmin), bins=41, fill='gray60', color='gray60', alpha=0.75, size=0.1)\n",
    "# flex.plot <- insert_xaxis_grob(flex.plot, hist, position = \"bottom\", height = grid::unit(0.1, \"null\"))\n",
    "flex.plot <- ggdraw(flex.plot)\n",
    "# ggsave(\"flexplot_midsleep.pdf\")\n",
    "flex.plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{Binned Nighttime Minimum Temperature Sleep Onset, Midsleep and Offset Regressions} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{1}{c}{Dependent Variable: Sleep Timing (Minutes)} \\\\ \n",
      "\\cline{2-2} \n",
      " & Change in Sleep Midsleep Time \\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " CUTTMIN(-Inf,-15] & 1.084 \\\\ \n",
      "  & (0.843) \\\\ \n",
      "  CUTTMIN(-15,-10] & $-$0.794 \\\\ \n",
      "  & (0.623) \\\\ \n",
      "  CUTTMIN(-10,-5] & $-$1.824$^{***}$ \\\\ \n",
      "  & (0.422) \\\\ \n",
      "  CUTTMIN(-5,0] & $-$1.310$^{***}$ \\\\ \n",
      "  & (0.390) \\\\ \n",
      "  CUTTMIN(0,5] & $-$0.851$^{***}$ \\\\ \n",
      "  & (0.241) \\\\ \n",
      "  CUTTMIN(10,15] & 0.950$^{***}$ \\\\ \n",
      "  & (0.246) \\\\ \n",
      "  CUTTMIN(15,20] & 1.061$^{***}$ \\\\ \n",
      "  & (0.388) \\\\ \n",
      "  CUTTMIN(20,25] & 0.983$^{**}$ \\\\ \n",
      "  & (0.455) \\\\ \n",
      "  CUTTMIN(25, Inf] & 1.005$^{*}$ \\\\ \n",
      "  & (0.592) \\\\ \n",
      "  tmin\\_norm\\_w7 & 0.161 \\\\ \n",
      "  & (0.109) \\\\ \n",
      "  CUTPRCP(0,1] & 0.768$^{***}$ \\\\ \n",
      "  & (0.137) \\\\ \n",
      "  CUTPRCP(1,2] & 1.375$^{***}$ \\\\ \n",
      "  & (0.294) \\\\ \n",
      "  CUTPRCP(2,3] & 1.947$^{***}$ \\\\ \n",
      "  & (0.319) \\\\ \n",
      "  CUTPRCP(3, Inf] & 0.876 \\\\ \n",
      "  & (0.543) \\\\ \n",
      "  prcp\\_norm\\_w7 & 4.963$^{***}$ \\\\ \n",
      "  & (1.228) \\\\ \n",
      "  CUTTRANGE(-Inf,0] & $-$1.510 \\\\ \n",
      "  & (1.415) \\\\ \n",
      "  CUTTRANGE(0,5] & 0.328$^{*}$ \\\\ \n",
      "  & (0.173) \\\\ \n",
      "  CUTTRANGE(10,15] & $-$0.222 \\\\ \n",
      "  & (0.188) \\\\ \n",
      "  CUTTRANGE(15,20] & $-$0.888$^{***}$ \\\\ \n",
      "  & (0.301) \\\\ \n",
      "  CUTTRANGE(20, Inf] & $-$1.724$^{**}$ \\\\ \n",
      "  & (0.755) \\\\ \n",
      "  trange\\_norm\\_w7 & 0.024 \\\\ \n",
      "  & (0.156) \\\\ \n",
      "  CUTCLOUD\\_rean2(0,20] & $-$0.087 \\\\ \n",
      "  & (0.513) \\\\ \n",
      "  CUTCLOUD\\_rean2(20,40] & $-$0.495 \\\\ \n",
      "  & (0.544) \\\\ \n",
      "  CUTCLOUD\\_rean2(40,60] & $-$0.473 \\\\ \n",
      "  & (0.559) \\\\ \n",
      "  CUTCLOUD\\_rean2(60,80] & $-$0.074 \\\\ \n",
      "  & (0.566) \\\\ \n",
      "  CUTCLOUD\\_rean2(80,100] & 0.742 \\\\ \n",
      "  & (0.538) \\\\ \n",
      "  cloud\\_norm\\_7\\_rean2 & 0.077$^{***}$ \\\\ \n",
      "  & (0.028) \\\\ \n",
      "  CUTRHUM\\_rean2(0,20] & 0.019 \\\\ \n",
      "  & (2.291) \\\\ \n",
      "  CUTRHUM\\_rean2(20,40] & 0.599 \\\\ \n",
      "  & (0.792) \\\\ \n",
      "  CUTRHUM\\_rean2(40,60] & 0.227 \\\\ \n",
      "  & (0.273) \\\\ \n",
      "  CUTRHUM\\_rean2(80,100] & 0.152 \\\\ \n",
      "  & (0.190) \\\\ \n",
      "  rhum\\_norm\\_7\\_rean2 & $-$0.207$^{***}$ \\\\ \n",
      "  & (0.057) \\\\ \n",
      "  CUTWIND\\_rean2(5,10] & 0.980$^{***}$ \\\\ \n",
      "  & (0.131) \\\\ \n",
      "  CUTWIND\\_rean2(10,Inf] & 1.935$^{***}$ \\\\ \n",
      "  & (0.261) \\\\ \n",
      "  wind\\_norm\\_7\\_rean2 & $-$0.197 \\\\ \n",
      "  & (0.170) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes \\\\ \n",
      "Date FE & Yes \\\\ \n",
      "State:Month FE & Yes \\\\ \n",
      "Observations & 4,405,171 \\\\ \n",
      "R$^{2}$ & 0.507 \\\\ \n",
      "Adjusted R$^{2}$ & 0.501 \\\\ \n",
      "Residual Std. Error & 67.081 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{1}{r}{Standard errors are in parentheses and are clustered on the first admin level} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# invisible(stargazer(flex_t_midsleep,\n",
    "#           type='latex',\n",
    "#           title = \"Binned Nighttime Minimum Temperature Sleep Onset, Midsleep and Offset Regressions\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Change in Sleep Midsleep Time'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Timing (Minutes)',\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the first admin level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#FIG 2E - Temperature vs. Sleep Onset FE Reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = onset ~ CUTTMIN + tmin_norm_w7 + CUTPRCP + prcp_norm_w7 +      CUTTRANGE + trange_norm_w7 + CUTCLOUD_rean2 + cloud_norm_7_rean2 +      CUTRHUM_rean2 + rhum_norm_7_rean2 + CUTWIND_rean2 + wind_norm_7_rean2 |      userID + unique_day_no + stateyrmn | 0 | state, data = Climate_Controls_DF,      na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "     Min       1Q   Median       3Q      Max \n",
       "-1699.11   -37.54     4.41    49.76   990.29 \n",
       "\n",
       "Coefficients:\n",
       "                       Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "CUTTMIN(-Inf,-15]      -0.93934      1.13980  -0.824 0.409866    \n",
       "CUTTMIN(-15,-10]       -1.67537      1.04474  -1.604 0.108797    \n",
       "CUTTMIN(-10,-5]        -2.30878      0.70825  -3.260 0.001115 ** \n",
       "CUTTMIN(-5,0]          -1.96177      0.57164  -3.432 0.000600 ***\n",
       "CUTTMIN(0,5]           -1.31891      0.31533  -4.183 2.88e-05 ***\n",
       "CUTTMIN(10,15]          2.25530      0.37005   6.095 1.10e-09 ***\n",
       "CUTTMIN(15,20]          3.45399      0.50990   6.774 1.25e-11 ***\n",
       "CUTTMIN(20,25]          4.84777      0.76805   6.312 2.76e-10 ***\n",
       "CUTTMIN(25, Inf]        5.91808      0.95433   6.201 5.60e-10 ***\n",
       "tmin_norm_w7            0.13107      0.12914   1.015 0.310132    \n",
       "CUTPRCP(0,1]            0.59548      0.18706   3.183 0.001456 ** \n",
       "CUTPRCP(1,2]            0.67027      0.33561   1.997 0.045808 *  \n",
       "CUTPRCP(2,3]            1.18197      0.57990   2.038 0.041529 *  \n",
       "CUTPRCP(3, Inf]         0.65001      0.94912   0.685 0.493437    \n",
       "prcp_norm_w7            3.58851      1.54434   2.324 0.020144 *  \n",
       "CUTTRANGE(-Inf,0]      -3.24340      2.37705  -1.364 0.172422    \n",
       "CUTTRANGE(0,5]         -0.13051      0.20555  -0.635 0.525475    \n",
       "CUTTRANGE(10,15]        0.20408      0.22096   0.924 0.355693    \n",
       "CUTTRANGE(15,20]        0.21045      0.47132   0.447 0.655229    \n",
       "CUTTRANGE(20, Inf]      0.16258      1.37554   0.118 0.905916    \n",
       "trange_norm_w7          0.03297      0.24479   0.135 0.892856    \n",
       "CUTCLOUD_rean2(0,20]    0.59713      0.75990   0.786 0.431989    \n",
       "CUTCLOUD_rean2(20,40]   0.15621      0.77052   0.203 0.839344    \n",
       "CUTCLOUD_rean2(40,60]   0.18058      0.76722   0.235 0.813919    \n",
       "CUTCLOUD_rean2(60,80]   0.40574      0.80149   0.506 0.612695    \n",
       "CUTCLOUD_rean2(80,100]  1.03684      0.80056   1.295 0.195272    \n",
       "cloud_norm_7_rean2      0.15643      0.03982   3.928 8.56e-05 ***\n",
       "CUTRHUM_rean2(0,20]     4.00307      2.91187   1.375 0.169212    \n",
       "CUTRHUM_rean2(20,40]    3.13345      1.10260   2.842 0.004485 ** \n",
       "CUTRHUM_rean2(40,60]    0.67141      0.34880   1.925 0.054239 .  \n",
       "CUTRHUM_rean2(80,100]   0.46015      0.25714   1.789 0.073535 .  \n",
       "rhum_norm_7_rean2      -0.38739      0.07620  -5.084 3.70e-07 ***\n",
       "CUTWIND_rean2(5,10]     0.51932      0.19906   2.609 0.009085 ** \n",
       "CUTWIND_rean2(10,Inf]   1.28511      0.37795   3.400 0.000673 ***\n",
       "wind_norm_7_rean2      -0.16941      0.31772  -0.533 0.593892    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 137.1 on 4345329 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2363   Adjusted R-squared: 0.2258 \n",
       "Multiple R-squared(proj model): 7.686e-05   Adjusted R-squared: -0.01369 \n",
       "F-statistic(full model, *iid*):22.47 on 59841 and 4345329 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 8.007 on 35 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# flex_t_onset <- felm(formula = onset ~ CUTTMIN + tmin_norm_w7 + \n",
    "#                                           CUTPRCP + prcp_norm_w7 + \n",
    "#                                           CUTTRANGE + trange_norm_w7 + \n",
    "#                                           CUTCLOUD_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           CUTRHUM_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           CUTWIND_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(flex_t_onset)\n",
    "\n",
    "# #save model coefficients for plotting\n",
    "# dt <- as.data.frame(summary(flex_t_onset)$coefficients[, 1:4]) # extract from felm summary (use df to keep coefficient\n",
    "# write.csv(dt,file=\"model_data/Fig2E_binned_tmin_sleep_onset_model_output.csv\")#save data as CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
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bFZWVnff//92bNnq6qqNBqNXff++OOP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4Bb8/3P5tZt4eEBAwYsQIi3MWFRWtX7+e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\nAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt <- read.csv(file=\"model_data/Fig2E_binned_tmin_sleep_onset_model_output.csv\",header=T)\n",
    "flex.plot <- binned.plot(felm.est = flex_t_onset,\n",
    "                 plotvar = \"CUTTMIN\",\n",
    "                 breaks = 5,\n",
    "                 omit = c(5,10),\n",
    "                 ylimit = c(-4,7.6),\n",
    "                 linecolor = \"purple\",\n",
    "                 pointfill = \"purple\",\n",
    "                 errorfill = \"purple\",\n",
    "                 xlabel = \"Min. Temperature in C\",\n",
    "                 ylabel = \"Change in Sleep Onset Time (Minutes)\"\n",
    "                 )\n",
    "# hist <- axis_canvas(flex.plot, axis = \"x\") + \n",
    "#         geom_histogram(data = Climate_Controls_DF, aes(x = Climate_Controls_DF$tmin), bins=41, fill='gray60', color='gray60', alpha=0.75, size=0.1)\n",
    "# flex.plot <- insert_xaxis_grob(flex.plot, hist, position = \"bottom\", height = grid::unit(0.1, \"null\"))\n",
    "flex.plot <- ggdraw(flex.plot)\n",
    "#ggsave(\"flexplot_onset.pdf\")\n",
    "flex.plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{Binned Nighttime Minimum Temperature Sleep Onset, Midsleep and Offset Regressions} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{1}{c}{Dependent Variable: Sleep Timing (Minutes)} \\\\ \n",
      "\\cline{2-2} \n",
      " & Change in Sleep Onset Time \\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " CUTTMIN(-Inf,-15] & $-$0.939 \\\\ \n",
      "  & (1.140) \\\\ \n",
      "  CUTTMIN(-15,-10] & $-$1.675 \\\\ \n",
      "  & (1.045) \\\\ \n",
      "  CUTTMIN(-10,-5] & $-$2.309$^{***}$ \\\\ \n",
      "  & (0.708) \\\\ \n",
      "  CUTTMIN(-5,0] & $-$1.962$^{***}$ \\\\ \n",
      "  & (0.572) \\\\ \n",
      "  CUTTMIN(0,5] & $-$1.319$^{***}$ \\\\ \n",
      "  & (0.315) \\\\ \n",
      "  CUTTMIN(10,15] & 2.255$^{***}$ \\\\ \n",
      "  & (0.370) \\\\ \n",
      "  CUTTMIN(15,20] & 3.454$^{***}$ \\\\ \n",
      "  & (0.510) \\\\ \n",
      "  CUTTMIN(20,25] & 4.848$^{***}$ \\\\ \n",
      "  & (0.768) \\\\ \n",
      "  CUTTMIN(25, Inf] & 5.918$^{***}$ \\\\ \n",
      "  & (0.954) \\\\ \n",
      "  tmin\\_norm\\_w7 & 0.131 \\\\ \n",
      "  & (0.129) \\\\ \n",
      "  CUTPRCP(0,1] & 0.595$^{***}$ \\\\ \n",
      "  & (0.187) \\\\ \n",
      "  CUTPRCP(1,2] & 0.670$^{**}$ \\\\ \n",
      "  & (0.336) \\\\ \n",
      "  CUTPRCP(2,3] & 1.182$^{**}$ \\\\ \n",
      "  & (0.580) \\\\ \n",
      "  CUTPRCP(3, Inf] & 0.650 \\\\ \n",
      "  & (0.949) \\\\ \n",
      "  prcp\\_norm\\_w7 & 3.589$^{**}$ \\\\ \n",
      "  & (1.544) \\\\ \n",
      "  CUTTRANGE(-Inf,0] & $-$3.243 \\\\ \n",
      "  & (2.377) \\\\ \n",
      "  CUTTRANGE(0,5] & $-$0.131 \\\\ \n",
      "  & (0.206) \\\\ \n",
      "  CUTTRANGE(10,15] & 0.204 \\\\ \n",
      "  & (0.221) \\\\ \n",
      "  CUTTRANGE(15,20] & 0.210 \\\\ \n",
      "  & (0.471) \\\\ \n",
      "  CUTTRANGE(20, Inf] & 0.163 \\\\ \n",
      "  & (1.376) \\\\ \n",
      "  trange\\_norm\\_w7 & 0.033 \\\\ \n",
      "  & (0.245) \\\\ \n",
      "  CUTCLOUD\\_rean2(0,20] & 0.597 \\\\ \n",
      "  & (0.760) \\\\ \n",
      "  CUTCLOUD\\_rean2(20,40] & 0.156 \\\\ \n",
      "  & (0.771) \\\\ \n",
      "  CUTCLOUD\\_rean2(40,60] & 0.181 \\\\ \n",
      "  & (0.767) \\\\ \n",
      "  CUTCLOUD\\_rean2(60,80] & 0.406 \\\\ \n",
      "  & (0.801) \\\\ \n",
      "  CUTCLOUD\\_rean2(80,100] & 1.037 \\\\ \n",
      "  & (0.801) \\\\ \n",
      "  cloud\\_norm\\_7\\_rean2 & 0.156$^{***}$ \\\\ \n",
      "  & (0.040) \\\\ \n",
      "  CUTRHUM\\_rean2(0,20] & 4.003 \\\\ \n",
      "  & (2.912) \\\\ \n",
      "  CUTRHUM\\_rean2(20,40] & 3.133$^{***}$ \\\\ \n",
      "  & (1.103) \\\\ \n",
      "  CUTRHUM\\_rean2(40,60] & 0.671$^{*}$ \\\\ \n",
      "  & (0.349) \\\\ \n",
      "  CUTRHUM\\_rean2(80,100] & 0.460$^{*}$ \\\\ \n",
      "  & (0.257) \\\\ \n",
      "  rhum\\_norm\\_7\\_rean2 & $-$0.387$^{***}$ \\\\ \n",
      "  & (0.076) \\\\ \n",
      "  CUTWIND\\_rean2(5,10] & 0.519$^{***}$ \\\\ \n",
      "  & (0.199) \\\\ \n",
      "  CUTWIND\\_rean2(10,Inf] & 1.285$^{***}$ \\\\ \n",
      "  & (0.378) \\\\ \n",
      "  wind\\_norm\\_7\\_rean2 & $-$0.169 \\\\ \n",
      "  & (0.318) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes \\\\ \n",
      "Date FE & Yes \\\\ \n",
      "State:Month FE & Yes \\\\ \n",
      "Observations & 4,405,171 \\\\ \n",
      "R$^{2}$ & 0.236 \\\\ \n",
      "Adjusted R$^{2}$ & 0.226 \\\\ \n",
      "Residual Std. Error & 137.062 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{1}{r}{Standard errors are in parentheses and are clustered on the first admin level} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# invisible(stargazer(flex_t_onset,\n",
    "#           type='latex',\n",
    "#           title = \"Binned Nighttime Minimum Temperature Sleep Onset, Midsleep and Offset Regressions\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Change in Sleep Onset Time'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Timing (Minutes)',\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the first admin level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#FIG 2D - Temperature vs. Short Sleep FE Regs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = short_7hr_sleep ~ CUTTMIN + tmin_norm_w7 + CUTPRCP +      prcp_norm_w7 + CUTTRANGE + trange_norm_w7 + CUTCLOUD_rean2 +      cloud_norm_7_rean2 + CUTRHUM_rean2 + rhum_norm_7_rean2 +      CUTWIND_rean2 + wind_norm_7_rean2 | userID + unique_day_no +      stateyrmn | 0 | state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "     Min       1Q   Median       3Q      Max \n",
       "-1.33320 -0.37426  0.04254  0.35731  1.29861 \n",
       "\n",
       "Coefficients:\n",
       "                         Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "CUTTMIN(-Inf,-15]      -0.0111529    0.0047574  -2.344 0.019061 *  \n",
       "CUTTMIN(-15,-10]       -0.0091200    0.0031660  -2.881 0.003969 ** \n",
       "CUTTMIN(-10,-5]        -0.0061411    0.0024083  -2.550 0.010773 *  \n",
       "CUTTMIN(-5,0]          -0.0022252    0.0018889  -1.178 0.238773    \n",
       "CUTTMIN(0,5]           -0.0039407    0.0011661  -3.379 0.000727 ***\n",
       "CUTTMIN(10,15]          0.0085149    0.0012553   6.783 1.18e-11 ***\n",
       "CUTTMIN(15,20]          0.0188294    0.0016821  11.194  < 2e-16 ***\n",
       "CUTTMIN(20,25]          0.0301216    0.0024083  12.507  < 2e-16 ***\n",
       "CUTTMIN(25, Inf]        0.0346247    0.0031493  10.994  < 2e-16 ***\n",
       "tmin_norm_w7            0.0010052    0.0004413   2.278 0.022750 *  \n",
       "CUTPRCP(0,1]           -0.0011536    0.0006996  -1.649 0.099161 .  \n",
       "CUTPRCP(1,2]           -0.0046414    0.0015386  -3.017 0.002556 ** \n",
       "CUTPRCP(2,3]           -0.0038732    0.0018306  -2.116 0.034358 *  \n",
       "CUTPRCP(3, Inf]        -0.0064375    0.0026870  -2.396 0.016584 *  \n",
       "prcp_norm_w7           -0.0057915    0.0054938  -1.054 0.291795    \n",
       "CUTTRANGE(-Inf,0]      -0.0183007    0.0072668  -2.518 0.011789 *  \n",
       "CUTTRANGE(0,5]         -0.0018210    0.0008359  -2.178 0.029375 *  \n",
       "CUTTRANGE(10,15]        0.0023190    0.0006127   3.785 0.000154 ***\n",
       "CUTTRANGE(15,20]        0.0086261    0.0013879   6.215 5.13e-10 ***\n",
       "CUTTRANGE(20, Inf]      0.0127218    0.0042169   3.017 0.002554 ** \n",
       "trange_norm_w7         -0.0001406    0.0008459  -0.166 0.868005    \n",
       "CUTCLOUD_rean2(0,20]   -0.0043370    0.0024358  -1.780 0.074995 .  \n",
       "CUTCLOUD_rean2(20,40]  -0.0035458    0.0026499  -1.338 0.180871    \n",
       "CUTCLOUD_rean2(40,60]  -0.0053169    0.0025445  -2.090 0.036659 *  \n",
       "CUTCLOUD_rean2(60,80]  -0.0067224    0.0026437  -2.543 0.010996 *  \n",
       "CUTCLOUD_rean2(80,100] -0.0086116    0.0027795  -3.098 0.001946 ** \n",
       "cloud_norm_7_rean2      0.0001241    0.0001027   1.208 0.227015    \n",
       "CUTRHUM_rean2(0,20]     0.0153978    0.0075205   2.047 0.040614 *  \n",
       "CUTRHUM_rean2(20,40]    0.0091805    0.0033926   2.706 0.006809 ** \n",
       "CUTRHUM_rean2(40,60]   -0.0002378    0.0009810  -0.242 0.808452    \n",
       "CUTRHUM_rean2(80,100]   0.0019246    0.0006677   2.883 0.003945 ** \n",
       "rhum_norm_7_rean2      -0.0006721    0.0001962  -3.425 0.000614 ***\n",
       "CUTWIND_rean2(5,10]    -0.0019075    0.0007073  -2.697 0.007001 ** \n",
       "CUTWIND_rean2(10,Inf]  -0.0029021    0.0012034  -2.412 0.015884 *  \n",
       "wind_norm_7_rean2       0.0017483    0.0008176   2.138 0.032491 *  \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 0.44 on 4345329 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2356   Adjusted R-squared: 0.2251 \n",
       "Multiple R-squared(proj model): 0.0001188   Adjusted R-squared: -0.01365 \n",
       "F-statistic(full model, *iid*):22.38 on 59841 and 4345329 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 15.33 on 35 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #flex_prob_7hr \n",
    "# flex_prob_7hr <- felm(formula = short_7hr_sleep ~ CUTTMIN + tmin_norm_w7 + \n",
    "#                                           CUTPRCP + prcp_norm_w7 + \n",
    "#                                           CUTTRANGE + trange_norm_w7 + \n",
    "#                                           CUTCLOUD_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           CUTRHUM_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           CUTWIND_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(flex_prob_7hr)\n",
    "\n",
    "# #save model coefficients for plotting\n",
    "# dt <- as.data.frame(summary(flex_prob_7hr)$coefficients[, 1:4]) # extract from felm summary (use df to keep coefficient\n",
    "# write.csv(dt,file=\"model_data/Fig2D_binned_tmin_short_7hr_sleep_probability_model_output.csv\")#save data as CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 2286003 rows containing non-finite values (stat_bin).”Warning message:\n",
      "“Removed 2 rows containing missing values (geom_bar).”"
     ]
    },
    {
     "data": {
      "image/png": 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//UqVMVCk9L/ykUiilTpuzfvz8qKkp6AERERBQgbCZc/RZFK2EtraubTAXdMET/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GxnLv7Wk0Gu4PpVQquT+UwWBw+0+ltH8T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\nmCmSDRs2rFu3Tv855eXl77zzjpkCMK2FCxd+8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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#binned flex_prob_7hr plots\n",
    "dt <- read.csv(file=\"model_data/Fig2D_binned_tmin_short_7hr_sleep_probability_model_output.csv\",header=T)\n",
    "flex.plot <- binned.plot(felm.est = flex_prob_7hr,\n",
    "                 plotvar = \"CUTTMIN\",\n",
    "                 breaks = 5,\n",
    "                 omit = c(5,10),\n",
    "                 ylimit = c(-.022,.04),\n",
    "                 linecolor = \"#ffdb58\",\n",
    "                 pointfill = \"#ffdb58\",\n",
    "                 errorfill = \"#ffdb58\",\n",
    "                 xlabel = \"Min. Temperature in C\",\n",
    "                 ylabel = \"Change in Probability of <7hr of sleep\"\n",
    "                 )\n",
    "# hist <- axis_canvas(flex.plot, axis = \"x\") + \n",
    "#         geom_histogram(data = Climate_Controls_DF, aes(x = Climate_Controls_DF$tmin), bins=41, fill='gray60', color='gray60', alpha=0.75, size=0.1)\n",
    "# flex.plot <- insert_xaxis_grob(flex.plot, hist, position = \"bottom\", height = grid::unit(0.1, \"null\"))\n",
    "flex.plot <- ggdraw(flex.plot)\n",
    "#ggsave(\"flexplot_prob_7hr.pdf\")\n",
    "flex.plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{Nighttime Minimum Temperature and Short Sleep Binned Probability Models} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{1}{c}{Dependent Variable: Short Sleep Attainment <7hr} \\\\ \n",
      "\\cline{2-2} \n",
      " & Change in Prob <7 hrs Sleep \\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " CUTTMIN(-Inf,-15] & $-$0.011$^{**}$ \\\\ \n",
      "  & (0.005) \\\\ \n",
      "  CUTTMIN(-15,-10] & $-$0.009$^{***}$ \\\\ \n",
      "  & (0.003) \\\\ \n",
      "  CUTTMIN(-10,-5] & $-$0.006$^{**}$ \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  CUTTMIN(-5,0] & $-$0.002 \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  CUTTMIN(0,5] & $-$0.004$^{***}$ \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTTMIN(10,15] & 0.009$^{***}$ \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTTMIN(15,20] & 0.019$^{***}$ \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  CUTTMIN(20,25] & 0.030$^{***}$ \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  CUTTMIN(25, Inf] & 0.035$^{***}$ \\\\ \n",
      "  & (0.003) \\\\ \n",
      "  tmin\\_norm\\_w7 & 0.001$^{**}$ \\\\ \n",
      "  & (0.0004) \\\\ \n",
      "  CUTPRCP(0,1] & $-$0.001$^{*}$ \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTPRCP(1,2] & $-$0.005$^{***}$ \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  CUTPRCP(2,3] & $-$0.004$^{**}$ \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  CUTPRCP(3, Inf] & $-$0.006$^{**}$ \\\\ \n",
      "  & (0.003) \\\\ \n",
      "  prcp\\_norm\\_w7 & $-$0.006 \\\\ \n",
      "  & (0.005) \\\\ \n",
      "  CUTTRANGE(-Inf,0] & $-$0.018$^{**}$ \\\\ \n",
      "  & (0.007) \\\\ \n",
      "  CUTTRANGE(0,5] & $-$0.002$^{**}$ \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTTRANGE(10,15] & 0.002$^{***}$ \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTTRANGE(15,20] & 0.009$^{***}$ \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTTRANGE(20, Inf] & 0.013$^{***}$ \\\\ \n",
      "  & (0.004) \\\\ \n",
      "  trange\\_norm\\_w7 & $-$0.0001 \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTCLOUD\\_rean2(0,20] & $-$0.004$^{*}$ \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  CUTCLOUD\\_rean2(20,40] & $-$0.004 \\\\ \n",
      "  & (0.003) \\\\ \n",
      "  CUTCLOUD\\_rean2(40,60] & $-$0.005$^{**}$ \\\\ \n",
      "  & (0.003) \\\\ \n",
      "  CUTCLOUD\\_rean2(60,80] & $-$0.007$^{**}$ \\\\ \n",
      "  & (0.003) \\\\ \n",
      "  CUTCLOUD\\_rean2(80,100] & $-$0.009$^{***}$ \\\\ \n",
      "  & (0.003) \\\\ \n",
      "  cloud\\_norm\\_7\\_rean2 & 0.0001 \\\\ \n",
      "  & (0.0001) \\\\ \n",
      "  CUTRHUM\\_rean2(0,20] & 0.015$^{**}$ \\\\ \n",
      "  & (0.008) \\\\ \n",
      "  CUTRHUM\\_rean2(20,40] & 0.009$^{***}$ \\\\ \n",
      "  & (0.003) \\\\ \n",
      "  CUTRHUM\\_rean2(40,60] & $-$0.0002 \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTRHUM\\_rean2(80,100] & 0.002$^{***}$ \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  rhum\\_norm\\_7\\_rean2 & $-$0.001$^{***}$ \\\\ \n",
      "  & (0.0002) \\\\ \n",
      "  CUTWIND\\_rean2(5,10] & $-$0.002$^{***}$ \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTWIND\\_rean2(10,Inf] & $-$0.003$^{**}$ \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  wind\\_norm\\_7\\_rean2 & 0.002$^{**}$ \\\\ \n",
      "  & (0.001) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes \\\\ \n",
      "Date FE & Yes \\\\ \n",
      "State:Month FE & Yes \\\\ \n",
      "Observations & 4,405,171 \\\\ \n",
      "R$^{2}$ & 0.236 \\\\ \n",
      "Adjusted R$^{2}$ & 0.225 \\\\ \n",
      "Residual Std. Error & 0.440 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{1}{r}{Standard errors are in parentheses and are clustered on the first admin level} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# invisible(stargazer(flex_prob_7hr,\n",
    "#           type='latex',\n",
    "#           title = \"Nighttime Minimum Temperature and Short Sleep Binned Probability Models\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Change in Prob <7 hrs Sleep'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Short Sleep Attainment <7hr',\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the first admin level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = short_6hr_sleep ~ CUTTMIN + tmin_norm_w7 + CUTPRCP +      prcp_norm_w7 + CUTTRANGE + trange_norm_w7 + CUTCLOUD_rean2 +      cloud_norm_7_rean2 + CUTRHUM_rean2 + rhum_norm_7_rean2 +      CUTWIND_rean2 + wind_norm_7_rean2 | userID + unique_day_no +      stateyrmn | 0 | state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-1.0319 -0.2536 -0.1096  0.2602  1.2570 \n",
       "\n",
       "Coefficients:\n",
       "                         Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "CUTTMIN(-Inf,-15]      -6.330e-03    3.565e-03  -1.776 0.075778 .  \n",
       "CUTTMIN(-15,-10]       -8.180e-03    2.872e-03  -2.848 0.004397 ** \n",
       "CUTTMIN(-10,-5]        -2.467e-03    1.935e-03  -1.274 0.202505    \n",
       "CUTTMIN(-5,0]          -1.220e-03    1.337e-03  -0.912 0.361733    \n",
       "CUTTMIN(0,5]           -4.190e-03    9.902e-04  -4.231 2.32e-05 ***\n",
       "CUTTMIN(10,15]          7.171e-03    1.257e-03   5.707 1.15e-08 ***\n",
       "CUTTMIN(15,20]          1.419e-02    1.474e-03   9.627  < 2e-16 ***\n",
       "CUTTMIN(20,25]          2.215e-02    2.276e-03   9.732  < 2e-16 ***\n",
       "CUTTMIN(25, Inf]        2.436e-02    2.818e-03   8.645  < 2e-16 ***\n",
       "tmin_norm_w7            2.666e-04    3.098e-04   0.861 0.389458    \n",
       "CUTPRCP(0,1]            2.257e-04    5.397e-04   0.418 0.675835    \n",
       "CUTPRCP(1,2]           -1.979e-03    1.199e-03  -1.651 0.098694 .  \n",
       "CUTPRCP(2,3]           -3.522e-03    1.493e-03  -2.359 0.018316 *  \n",
       "CUTPRCP(3, Inf]        -8.730e-03    3.151e-03  -2.771 0.005591 ** \n",
       "prcp_norm_w7            7.798e-03    4.856e-03   1.606 0.108265    \n",
       "CUTTRANGE(-Inf,0]      -8.956e-03    6.330e-03  -1.415 0.157136    \n",
       "CUTTRANGE(0,5]         -1.519e-03    7.556e-04  -2.011 0.044355 *  \n",
       "CUTTRANGE(10,15]        6.983e-04    5.494e-04   1.271 0.203777    \n",
       "CUTTRANGE(15,20]        4.552e-03    1.304e-03   3.490 0.000483 ***\n",
       "CUTTRANGE(20, Inf]      5.027e-03    3.556e-03   1.414 0.157478    \n",
       "trange_norm_w7         -4.978e-05    5.690e-04  -0.087 0.930287    \n",
       "CUTCLOUD_rean2(0,20]    8.654e-05    2.029e-03   0.043 0.965971    \n",
       "CUTCLOUD_rean2(20,40]   4.902e-04    2.123e-03   0.231 0.817375    \n",
       "CUTCLOUD_rean2(40,60]  -1.352e-03    2.106e-03  -0.642 0.520991    \n",
       "CUTCLOUD_rean2(60,80]  -1.653e-03    2.222e-03  -0.744 0.456948    \n",
       "CUTCLOUD_rean2(80,100] -2.692e-03    2.104e-03  -1.280 0.200618    \n",
       "cloud_norm_7_rean2      1.788e-04    1.184e-04   1.510 0.131059    \n",
       "CUTRHUM_rean2(0,20]     1.499e-02    7.294e-03   2.055 0.039925 *  \n",
       "CUTRHUM_rean2(20,40]    4.750e-03    2.967e-03   1.601 0.109307    \n",
       "CUTRHUM_rean2(40,60]   -7.439e-04    8.522e-04  -0.873 0.382669    \n",
       "CUTRHUM_rean2(80,100]   1.006e-03    5.293e-04   1.900 0.057423 .  \n",
       "rhum_norm_7_rean2      -4.099e-04    1.835e-04  -2.234 0.025491 *  \n",
       "CUTWIND_rean2(5,10]    -1.176e-04    6.125e-04  -0.192 0.847759    \n",
       "CUTWIND_rean2(10,Inf]   6.452e-04    9.476e-04   0.681 0.495986    \n",
       "wind_norm_7_rean2       5.964e-05    6.246e-04   0.095 0.923926    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 0.3982 on 4345329 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2012   Adjusted R-squared: 0.1902 \n",
       "Multiple R-squared(proj model): 6.787e-05   Adjusted R-squared: -0.0137 \n",
       "F-statistic(full model, *iid*):18.29 on 59841 and 4345329 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model):  9.85 on 35 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #flex_prob_6hr \n",
    "# flex_prob_6hr <- felm(formula = short_6hr_sleep ~ CUTTMIN + tmin_norm_w7 + \n",
    "#                                           CUTPRCP + prcp_norm_w7 + \n",
    "#                                           CUTTRANGE + trange_norm_w7 + \n",
    "#                                           CUTCLOUD_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           CUTRHUM_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           CUTWIND_rean2 + wind_norm_7_rean2  \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(flex_prob_6hr)\n",
    "# #save model coefficients for plotting\n",
    "# dt <- as.data.frame(summary(flex_prob_6hr)$coefficients[, 1:4]) # extract from felm summary (use df to keep coefficient\n",
    "# write.csv(dt,file=\"model_data/Fig2D_binned_tmin_short_6hr_sleep_probability_model_output.csv\")#save data as CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 2286003 rows containing non-finite values (stat_bin).”Warning message:\n",
      "“Removed 2 rows containing missing values (geom_bar).”"
     ]
    },
    {
     "data": {
      "image/png": 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11dXZ999ln9Izdv3tRte3p6CmmDiIiI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\njh8/XpvAAHh6er7++uv6F8fGxlq05vnz5/VPTZkyRZvqAPj6+s6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9e/c+frG3t3diYqKnp6fZbdgNrjxBRER/EJjqknYi4TPDp+Re6Lwe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+HQD8JiLiS1df3SbaQlyPBVsBUVv4rG/s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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#binned flex_prob_6hr plots\n",
    "dt <- read.csv(file=\"model_data/Fig2D_binned_tmin_short_6hr_sleep_probability_model_output.csv\",header=T)\n",
    "flex.plot <- binned.plot(felm.est = flex_prob_6hr,\n",
    "                 plotvar = \"CUTTMIN\",\n",
    "                 breaks = 5,\n",
    "                 omit = c(5,10),\n",
    "                 ylimit = c(-.022,.04),\n",
    "                 linecolor = \"orange\",\n",
    "                 pointfill = \"orange\",\n",
    "                 errorfill = \"orange\",\n",
    "                 xlabel = \"Min. Temperature in C\",\n",
    "                 ylabel = \"Change in Probability of <6hr of sleep\"\n",
    "                 )\n",
    "# hist <- axis_canvas(flex.plot, axis = \"x\") + \n",
    "#         geom_histogram(data = Climate_Controls_DF, aes(x = Climate_Controls_DF$tmin), bins=41, fill='gray60', color='gray60', alpha=0.75, size=0.1)\n",
    "# flex.plot <- insert_xaxis_grob(flex.plot, hist, position = \"bottom\", height = grid::unit(0.1, \"null\"))\n",
    "flex.plot <- ggdraw(flex.plot)\n",
    "#ggsave(\"flexplot_prob_6hr.pdf\")\n",
    "flex.plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{Nighttime Minimum Temperature and Short Sleep Binned Probability Models} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{1}{c}{Dependent Variable: Short Sleep Attainment <6hr} \\\\ \n",
      "\\cline{2-2} \n",
      " & Change in Prob <6 hrs Sleep \\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " CUTTMIN(-Inf,-15] & $-$0.006$^{*}$ \\\\ \n",
      "  & (0.004) \\\\ \n",
      "  CUTTMIN(-15,-10] & $-$0.008$^{***}$ \\\\ \n",
      "  & (0.003) \\\\ \n",
      "  CUTTMIN(-10,-5] & $-$0.002 \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  CUTTMIN(-5,0] & $-$0.001 \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTTMIN(0,5] & $-$0.004$^{***}$ \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTTMIN(10,15] & 0.007$^{***}$ \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTTMIN(15,20] & 0.014$^{***}$ \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTTMIN(20,25] & 0.022$^{***}$ \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  CUTTMIN(25, Inf] & 0.024$^{***}$ \\\\ \n",
      "  & (0.003) \\\\ \n",
      "  tmin\\_norm\\_w7 & 0.0003 \\\\ \n",
      "  & (0.0003) \\\\ \n",
      "  CUTPRCP(0,1] & 0.0002 \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTPRCP(1,2] & $-$0.002$^{*}$ \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTPRCP(2,3] & $-$0.004$^{**}$ \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTPRCP(3, Inf] & $-$0.009$^{***}$ \\\\ \n",
      "  & (0.003) \\\\ \n",
      "  prcp\\_norm\\_w7 & 0.008 \\\\ \n",
      "  & (0.005) \\\\ \n",
      "  CUTTRANGE(-Inf,0] & $-$0.009 \\\\ \n",
      "  & (0.006) \\\\ \n",
      "  CUTTRANGE(0,5] & $-$0.002$^{**}$ \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTTRANGE(10,15] & 0.001 \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTTRANGE(15,20] & 0.005$^{***}$ \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTTRANGE(20, Inf] & 0.005 \\\\ \n",
      "  & (0.004) \\\\ \n",
      "  trange\\_norm\\_w7 & $-$0.00005 \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTCLOUD\\_rean2(0,20] & 0.0001 \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  CUTCLOUD\\_rean2(20,40] & 0.0005 \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  CUTCLOUD\\_rean2(40,60] & $-$0.001 \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  CUTCLOUD\\_rean2(60,80] & $-$0.002 \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  CUTCLOUD\\_rean2(80,100] & $-$0.003 \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  cloud\\_norm\\_7\\_rean2 & 0.0002 \\\\ \n",
      "  & (0.0001) \\\\ \n",
      "  CUTRHUM\\_rean2(0,20] & 0.015$^{**}$ \\\\ \n",
      "  & (0.007) \\\\ \n",
      "  CUTRHUM\\_rean2(20,40] & 0.005 \\\\ \n",
      "  & (0.003) \\\\ \n",
      "  CUTRHUM\\_rean2(40,60] & $-$0.001 \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTRHUM\\_rean2(80,100] & 0.001$^{*}$ \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  rhum\\_norm\\_7\\_rean2 & $-$0.0004$^{**}$ \\\\ \n",
      "  & (0.0002) \\\\ \n",
      "  CUTWIND\\_rean2(5,10] & $-$0.0001 \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTWIND\\_rean2(10,Inf] & 0.001 \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  wind\\_norm\\_7\\_rean2 & 0.0001 \\\\ \n",
      "  & (0.001) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes \\\\ \n",
      "Date FE & Yes \\\\ \n",
      "State:Month FE & Yes \\\\ \n",
      "Observations & 4,405,171 \\\\ \n",
      "R$^{2}$ & 0.201 \\\\ \n",
      "Adjusted R$^{2}$ & 0.190 \\\\ \n",
      "Residual Std. Error & 0.398 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{1}{r}{Standard errors are in parentheses and are clustered on the first admin level} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# invisible(stargazer(flex_prob_6hr,\n",
    "#           type='latex',\n",
    "#           title = \"Nighttime Minimum Temperature and Short Sleep Binned Probability Models\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Change in Prob <6 hrs Sleep'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Short Sleep Attainment <6hr',\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the first admin level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = short_5hr_sleep ~ CUTTMIN + tmin_norm_w7 + CUTPRCP +      prcp_norm_w7 + CUTTRANGE + trange_norm_w7 + CUTCLOUD_rean2 +      cloud_norm_7_rean2 + CUTRHUM_rean2 + rhum_norm_7_rean2 +      CUTWIND_rean2 + wind_norm_7_rean2 | userID + unique_day_no +      stateyrmn | 0 | state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "     Min       1Q   Median       3Q      Max \n",
       "-0.91383 -0.10807 -0.04914 -0.01405  1.10656 \n",
       "\n",
       "Coefficients:\n",
       "                         Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "CUTTMIN(-Inf,-15]      -1.686e-03    1.995e-03  -0.845 0.397996    \n",
       "CUTTMIN(-15,-10]       -6.458e-04    1.693e-03  -0.381 0.702853    \n",
       "CUTTMIN(-10,-5]        -2.622e-04    1.202e-03  -0.218 0.827302    \n",
       "CUTTMIN(-5,0]          -1.012e-03    6.773e-04  -1.494 0.135257    \n",
       "CUTTMIN(0,5]           -2.149e-03    6.233e-04  -3.447 0.000566 ***\n",
       "CUTTMIN(10,15]          2.948e-03    7.775e-04   3.791 0.000150 ***\n",
       "CUTTMIN(15,20]          7.125e-03    9.342e-04   7.627 2.40e-14 ***\n",
       "CUTTMIN(20,25]          1.144e-02    1.473e-03   7.769 7.92e-15 ***\n",
       "CUTTMIN(25, Inf]        1.233e-02    1.885e-03   6.539 6.20e-11 ***\n",
       "tmin_norm_w7           -2.630e-05    2.110e-04  -0.125 0.900781    \n",
       "CUTPRCP(0,1]            1.165e-04    3.463e-04   0.336 0.736646    \n",
       "CUTPRCP(1,2]           -1.175e-03    8.004e-04  -1.468 0.142100    \n",
       "CUTPRCP(2,3]            7.382e-04    1.209e-03   0.611 0.541337    \n",
       "CUTPRCP(3, Inf]        -3.506e-03    1.670e-03  -2.100 0.035737 *  \n",
       "prcp_norm_w7           -2.402e-03    3.461e-03  -0.694 0.487648    \n",
       "CUTTRANGE(-Inf,0]      -3.451e-03    3.327e-03  -1.037 0.299650    \n",
       "CUTTRANGE(0,5]         -1.784e-04    4.480e-04  -0.398 0.690489    \n",
       "CUTTRANGE(10,15]        2.091e-04    3.615e-04   0.578 0.563025    \n",
       "CUTTRANGE(15,20]        2.290e-03    6.376e-04   3.591 0.000330 ***\n",
       "CUTTRANGE(20, Inf]      1.508e-03    2.012e-03   0.749 0.453791    \n",
       "trange_norm_w7          4.240e-05    3.377e-04   0.126 0.900095    \n",
       "CUTCLOUD_rean2(0,20]   -4.319e-04    1.241e-03  -0.348 0.727851    \n",
       "CUTCLOUD_rean2(20,40]  -1.650e-04    1.284e-03  -0.129 0.897718    \n",
       "CUTCLOUD_rean2(40,60]  -5.507e-04    1.298e-03  -0.424 0.671429    \n",
       "CUTCLOUD_rean2(60,80]  -1.092e-03    1.260e-03  -0.867 0.386121    \n",
       "CUTCLOUD_rean2(80,100] -1.735e-03    1.404e-03  -1.235 0.216699    \n",
       "cloud_norm_7_rean2      1.016e-04    6.370e-05   1.595 0.110774    \n",
       "CUTRHUM_rean2(0,20]     7.235e-03    5.861e-03   1.234 0.217044    \n",
       "CUTRHUM_rean2(20,40]    1.828e-03    1.905e-03   0.959 0.337357    \n",
       "CUTRHUM_rean2(40,60]    2.311e-04    5.804e-04   0.398 0.690506    \n",
       "CUTRHUM_rean2(80,100]   5.390e-04    3.647e-04   1.478 0.139398    \n",
       "rhum_norm_7_rean2      -5.775e-07    1.390e-04  -0.004 0.996686    \n",
       "CUTWIND_rean2(5,10]     1.557e-04    3.696e-04   0.421 0.673655    \n",
       "CUTWIND_rean2(10,Inf]   1.150e-03    5.831e-04   1.972 0.048606 *  \n",
       "wind_norm_7_rean2       6.064e-04    4.771e-04   1.271 0.203744    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 0.2762 on 4345329 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.1145   Adjusted R-squared: 0.1023 \n",
       "Multiple R-squared(proj model): 3.367e-05   Adjusted R-squared: -0.01374 \n",
       "F-statistic(full model, *iid*):9.393 on 59841 and 4345329 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 6.983 on 35 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #flex_prob_5hr \n",
    "# flex_prob_5hr <- felm(formula = short_5hr_sleep ~ CUTTMIN + tmin_norm_w7 + \n",
    "#                                           CUTPRCP + prcp_norm_w7 + \n",
    "#                                           CUTTRANGE + trange_norm_w7 + \n",
    "#                                           CUTCLOUD_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           CUTRHUM_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           CUTWIND_rean2 + wind_norm_7_rean2  \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(flex_prob_5hr)\n",
    "\n",
    "# #save model coefficients for plotting\n",
    "# dt <- as.data.frame(summary(flex_prob_5hr)$coefficients[, 1:4]) # extract from felm summary (use df to keep coefficient\n",
    "# write.csv(dt,file=\"model_data/Fig2D_binned_tmin_short_5hr_sleep_probability_model_output.csv\")#save data as CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 2286003 rows containing non-finite values (stat_bin).”Warning message:\n",
      "“Removed 2 rows containing missing values (geom_bar).”"
     ]
    },
    {
     "data": {
      "image/png": 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yQkvPbaazqdrri4+L///W+ZlgMHDjTe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\nC9CDeiESKX8AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#binned flex_prob_5hr plots\n",
    "dt <- read.csv(file=\"model_data/Fig2D_binned_tmin_short_5hr_sleep_probability_model_output.csv\",header=T)\n",
    "flex.plot <- binned.plot(felm.est = flex_prob_5hr,\n",
    "                 plotvar = \"CUTTMIN\",\n",
    "                 breaks = 5,\n",
    "                 omit = c(5,10),\n",
    "                 ylimit = c(-.022,.04),\n",
    "                 linecolor = \"red\",\n",
    "                 pointfill = \"red\",\n",
    "                 errorfill = \"red\",\n",
    "                 xlabel = \"Min. Temperature in C\",\n",
    "                 ylabel = \"Change in Probability of <5hr of sleep\"\n",
    "                 )\n",
    "# hist <- axis_canvas(flex.plot, axis = \"x\") + \n",
    "#         geom_histogram(data = Climate_Controls_DF, aes(x = Climate_Controls_DF$tmin), bins=41, fill='gray60', color='gray60', alpha=0.75, size=0.1)\n",
    "# flex.plot <- insert_xaxis_grob(flex.plot, hist, position = \"bottom\", height = grid::unit(0.1, \"null\"))\n",
    "flex.plot <- ggdraw(flex.plot)\n",
    "#ggsave(\"flexplot_prob_5hr.pdf\")\n",
    "flex.plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{Nighttime Minimum Temperature and Short Sleep Binned Probability Models} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{1}{c}{Dependent Variable: Short Sleep Attainment <5hr} \\\\ \n",
      "\\cline{2-2} \n",
      " & Change in Prob <5 hrs Sleep \\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " CUTTMIN(-Inf,-15] & $-$0.002 \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  CUTTMIN(-15,-10] & $-$0.001 \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  CUTTMIN(-10,-5] & $-$0.0003 \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTTMIN(-5,0] & $-$0.001 \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTTMIN(0,5] & $-$0.002$^{***}$ \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTTMIN(10,15] & 0.003$^{***}$ \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTTMIN(15,20] & 0.007$^{***}$ \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTTMIN(20,25] & 0.011$^{***}$ \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTTMIN(25, Inf] & 0.012$^{***}$ \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  tmin\\_norm\\_w7 & $-$0.00003 \\\\ \n",
      "  & (0.0002) \\\\ \n",
      "  CUTPRCP(0,1] & 0.0001 \\\\ \n",
      "  & (0.0003) \\\\ \n",
      "  CUTPRCP(1,2] & $-$0.001 \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTPRCP(2,3] & 0.001 \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTPRCP(3, Inf] & $-$0.004$^{**}$ \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  prcp\\_norm\\_w7 & $-$0.002 \\\\ \n",
      "  & (0.003) \\\\ \n",
      "  CUTTRANGE(-Inf,0] & $-$0.003 \\\\ \n",
      "  & (0.003) \\\\ \n",
      "  CUTTRANGE(0,5] & $-$0.0002 \\\\ \n",
      "  & (0.0004) \\\\ \n",
      "  CUTTRANGE(10,15] & 0.0002 \\\\ \n",
      "  & (0.0004) \\\\ \n",
      "  CUTTRANGE(15,20] & 0.002$^{***}$ \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTTRANGE(20, Inf] & 0.002 \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  trange\\_norm\\_w7 & 0.00004 \\\\ \n",
      "  & (0.0003) \\\\ \n",
      "  CUTCLOUD\\_rean2(0,20] & $-$0.0004 \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTCLOUD\\_rean2(20,40] & $-$0.0002 \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTCLOUD\\_rean2(40,60] & $-$0.001 \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTCLOUD\\_rean2(60,80] & $-$0.001 \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTCLOUD\\_rean2(80,100] & $-$0.002 \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  cloud\\_norm\\_7\\_rean2 & 0.0001 \\\\ \n",
      "  & (0.0001) \\\\ \n",
      "  CUTRHUM\\_rean2(0,20] & 0.007 \\\\ \n",
      "  & (0.006) \\\\ \n",
      "  CUTRHUM\\_rean2(20,40] & 0.002 \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  CUTRHUM\\_rean2(40,60] & 0.0002 \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  CUTRHUM\\_rean2(80,100] & 0.001 \\\\ \n",
      "  & (0.0004) \\\\ \n",
      "  rhum\\_norm\\_7\\_rean2 & $-$0.00000 \\\\ \n",
      "  & (0.0001) \\\\ \n",
      "  CUTWIND\\_rean2(5,10] & 0.0002 \\\\ \n",
      "  & (0.0004) \\\\ \n",
      "  CUTWIND\\_rean2(10,Inf] & 0.001$^{**}$ \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  wind\\_norm\\_7\\_rean2 & 0.001 \\\\ \n",
      "  & (0.0005) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes \\\\ \n",
      "Date FE & Yes \\\\ \n",
      "State:Month FE & Yes \\\\ \n",
      "Observations & 4,405,171 \\\\ \n",
      "R$^{2}$ & 0.115 \\\\ \n",
      "Adjusted R$^{2}$ & 0.102 \\\\ \n",
      "Residual Std. Error & 0.276 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{1}{r}{Standard errors are in parentheses and are clustered on the first admin level} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# invisible(stargazer(flex_prob_5hr,\n",
    "#           type='latex',\n",
    "#           title = \"Nighttime Minimum Temperature and Short Sleep Binned Probability Models\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Change in Prob <5 hrs Sleep'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Short Sleep Attainment <5hr',\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the first admin level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#FIG 3A - Temperature Effects by Age Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmin + tmin:age_group_NSF +      tmin_norm_w7 + prcp + prcp_norm_w7 + trange + trange_norm_w7 +      cloud_rean2 + cloud_norm_7_rean2 + rhum_rean2 + rhum_norm_7_rean2 +      wind_rean2 + wind_norm_7_rean2 | userID + unique_day_no +      stateyrmn | 0 | state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-384.37  -49.12   -2.32   45.75  407.55 \n",
       "\n",
       "Coefficients:\n",
       "                                      Estimate Cluster s.e. t value Pr(>|t|)\n",
       "tmin                                -0.2832578    0.0268354 -10.555  < 2e-16\n",
       "tmin_norm_w7                        -0.1767861    0.0852413  -2.074  0.03808\n",
       "prcp                                 0.4841288    0.1203637   4.022 5.77e-05\n",
       "prcp_norm_w7                         2.1328768    1.0866965   1.963  0.04968\n",
       "trange                              -0.2077673    0.0226456  -9.175  < 2e-16\n",
       "trange_norm_w7                       0.0590536    0.1471616   0.401  0.68821\n",
       "cloud_rean2                          0.0101380    0.0023796   4.260 2.04e-05\n",
       "cloud_norm_7_rean2                  -0.0385011    0.0238177  -1.616  0.10599\n",
       "rhum_rean2                           0.0005178    0.0075290   0.069  0.94516\n",
       "rhum_norm_7_rean2                    0.1039120    0.0396024   2.624  0.00869\n",
       "wind_rean2                           0.1276757    0.0218442   5.845 5.07e-09\n",
       "wind_norm_7_rean2                   -0.2922024    0.1862605  -1.569  0.11670\n",
       "tmin:age_group_NSFage19_24_young    -0.0254954    0.0477557  -0.534  0.59343\n",
       "tmin:age_group_NSFage65_above_older -0.3235648    0.0415518  -7.787 6.86e-15\n",
       "                                       \n",
       "tmin                                ***\n",
       "tmin_norm_w7                        *  \n",
       "prcp                                ***\n",
       "prcp_norm_w7                        *  \n",
       "trange                              ***\n",
       "trange_norm_w7                         \n",
       "cloud_rean2                         ***\n",
       "cloud_norm_7_rean2                     \n",
       "rhum_rean2                             \n",
       "rhum_norm_7_rean2                   ** \n",
       "wind_rean2                          ***\n",
       "wind_norm_7_rean2                      \n",
       "tmin:age_group_NSFage19_24_young       \n",
       "tmin:age_group_NSFage65_above_older ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.71 on 4345350 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2842   Adjusted R-squared: 0.2744 \n",
       "Multiple R-squared(proj model): 0.0001822   Adjusted R-squared: -0.01358 \n",
       "F-statistic(full model, *iid*):28.84 on 59820 and 4345350 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 45.36 on 14 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #prepare age interaction model with National Sleep Foundation Age Groups\n",
    "# Climate_Controls_DF$age_group_NSF <- as.factor(Climate_Controls_DF$age_group_NSF)\n",
    "\n",
    "# #set middle adulthood as comparison\n",
    "# Climate_Controls_DF$age_group_NSF <- relevel(Climate_Controls_DF$age_group_NSF, ref=\"age25_64_adults\")\n",
    "# #levels(Climate_Controls_DF$age_group_NSF)\n",
    "\n",
    "# linear_t_age <- felm(formula = sleep_duration_minutes ~ tmin + tmin:age_group_NSF + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "# summary(linear_t_age)\n",
    "\n",
    "# #save model coefficients for plotting\n",
    "# dt <- as.data.frame(summary(linear_t_age)$coefficients[, 1:4]) # extract from felm summary (use df to keep coefficient\n",
    "# write.csv(dt,file=\"model_data/Fig_3A_linear_tmin_subgroup_by_age_group.csv\")#save data as CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Subgroups</th><th scope=col>Estimate</th><th scope=col>Cluster.s.e.</th><th scope=col>t.value</th><th scope=col>Pr...t..</th><th scope=col>base</th><th scope=col>plotcoef</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>age25_64    </td><td>-0.28325775 </td><td>0.02683541  </td><td>-10.5553736 </td><td>4.800760e-26</td><td>-0.2832578  </td><td>0.2832578   </td></tr>\n",
       "\t<tr><td>age19_24    </td><td>-0.02549536 </td><td>0.04775569  </td><td> -0.5338706 </td><td>5.934311e-01</td><td>-0.2832578  </td><td>0.3087531   </td></tr>\n",
       "\t<tr><td>age65_above </td><td>-0.32356484 </td><td>0.04155181  </td><td> -7.7870215 </td><td>6.862226e-15</td><td>-0.2832578  </td><td>0.6068226   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllll}\n",
       " Subgroups & Estimate & Cluster.s.e. & t.value & Pr...t.. & base & plotcoef\\\\\n",
       "\\hline\n",
       "\t age25\\_64    & -0.28325775   & 0.02683541    & -10.5553736   & 4.800760e-26  & -0.2832578    & 0.2832578    \\\\\n",
       "\t age19\\_24    & -0.02549536   & 0.04775569    &  -0.5338706   & 5.934311e-01  & -0.2832578    & 0.3087531    \\\\\n",
       "\t age65\\_above & -0.32356484   & 0.04155181    &  -7.7870215   & 6.862226e-15  & -0.2832578    & 0.6068226    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Subgroups | Estimate | Cluster.s.e. | t.value | Pr...t.. | base | plotcoef |\n",
       "|---|---|---|---|---|---|---|\n",
       "| age25_64     | -0.28325775  | 0.02683541   | -10.5553736  | 4.800760e-26 | -0.2832578   | 0.2832578    |\n",
       "| age19_24     | -0.02549536  | 0.04775569   |  -0.5338706  | 5.934311e-01 | -0.2832578   | 0.3087531    |\n",
       "| age65_above  | -0.32356484  | 0.04155181   |  -7.7870215  | 6.862226e-15 | -0.2832578   | 0.6068226    |\n",
       "\n"
      ],
      "text/plain": [
       "  Subgroups   Estimate    Cluster.s.e. t.value     Pr...t..     base      \n",
       "1 age25_64    -0.28325775 0.02683541   -10.5553736 4.800760e-26 -0.2832578\n",
       "2 age19_24    -0.02549536 0.04775569    -0.5338706 5.934311e-01 -0.2832578\n",
       "3 age65_above -0.32356484 0.04155181    -7.7870215 6.862226e-15 -0.2832578\n",
       "  plotcoef \n",
       "1 0.2832578\n",
       "2 0.3087531\n",
       "3 0.6068226"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACB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\nAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#load dt\n",
    "dt <- read.csv(file=\"model_data/Fig_3A_linear_tmin_subgroup_by_age_group.csv\",header=T)\n",
    "#Plot tmin results\n",
    "dt <- dt[grepl(\"tmin\", dt$X), ] # extract variable name (e.g., tmax.cut, ppt.cut, ...), limit to matches\n",
    "dt <- dt[!grepl(\"norm\", dt$X), ] #deselect norm variables\n",
    "dt <- as.data.table(dt)\n",
    "#recover marginal effects \n",
    "base<-dt[X==\"tmin\",.(Estimate)]\n",
    "dt[,base:=base]\n",
    "base<-dt[X==\"tmin\",.(Estimate)]\n",
    "dt[,plotcoef:=Estimate]\n",
    "dt[X!=\"tmin\",plotcoef:=plotcoef+base]\n",
    "dt[,plotcoef:=abs(plotcoef)] #plot absolute sleep loss\n",
    "#specify names for plotting\n",
    "dt[X==\"tmin\",X:=\"age25_64\"]\n",
    "dt[X==\"tmin:age_group_NSFage19_24_young\",X:=\"age19_24\"]\n",
    "dt[X==\"tmin:age_group_NSFage65_above_older\",X:=\"age65_above\"]\n",
    "setnames(dt,\"X\",\"Subgroups\")\n",
    "dt[,Subgroups:=as.factor(Subgroups)]\n",
    "dt\n",
    "barplot<-ggplot(dt) +\n",
    "  geom_bar(aes(x=Subgroups, y=plotcoef), stat=\"identity\", fill=\"#ffb600\", alpha=1) +\n",
    "  geom_errorbar(aes(x=Subgroups, ymin=plotcoef-Cluster.s.e.*1.96, ymax=plotcoef+Cluster.s.e.*1.96), width=0, alpha=0.9, size=1) +\n",
    "  scale_y_continuous(breaks=seq(0,1.5,0.5),limits=c(0,1.5),labels = scales::number_format(accuracy = 0.01)) +\n",
    "  theme_classic() +\n",
    "  ggtitle(\"Marginal Effect of Tmin by Age Group\")\n",
    "barplot\n",
    "#ggsave(\"barplot_marginal_effect_by_age_group.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmin + tmin:age_group_NSF +      tmin_norm_w7 + prcp + prcp_norm_w7 + trange + trange_norm_w7 +      cloud_rean2 + cloud_norm_7_rean2 + rhum_rean2 + rhum_norm_7_rean2 +      wind_rean2 + wind_norm_7_rean2 | userID + unique_day_no +      stateyrmn | 0 | state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-384.37  -49.12   -2.32   45.75  407.55 \n",
       "\n",
       "Coefficients:\n",
       "                                    Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin                              -0.6068226    0.0512520 -11.840  < 2e-16 ***\n",
       "tmin_norm_w7                      -0.1767861    0.0852413  -2.074  0.03808 *  \n",
       "prcp                               0.4841288    0.1203637   4.022 5.77e-05 ***\n",
       "prcp_norm_w7                       2.1328768    1.0866966   1.963  0.04968 *  \n",
       "trange                            -0.2077673    0.0226456  -9.175  < 2e-16 ***\n",
       "trange_norm_w7                     0.0590536    0.1471616   0.401  0.68821    \n",
       "cloud_rean2                        0.0101380    0.0023796   4.260 2.04e-05 ***\n",
       "cloud_norm_7_rean2                -0.0385011    0.0238177  -1.616  0.10599    \n",
       "rhum_rean2                         0.0005178    0.0075290   0.069  0.94516    \n",
       "rhum_norm_7_rean2                  0.1039120    0.0396024   2.624  0.00869 ** \n",
       "wind_rean2                         0.1276757    0.0218442   5.845 5.07e-09 ***\n",
       "wind_norm_7_rean2                 -0.2922024    0.1862605  -1.569  0.11670    \n",
       "tmin:age_group_NSFage25_64_adults  0.3235648    0.0415518   7.787 6.86e-15 ***\n",
       "tmin:age_group_NSFage19_24_young   0.2980695    0.0645765   4.616 3.92e-06 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.71 on 4345350 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2842   Adjusted R-squared: 0.2744 \n",
       "Multiple R-squared(proj model): 0.0001822   Adjusted R-squared: -0.01358 \n",
       "F-statistic(full model, *iid*):28.84 on 59820 and 4345350 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 45.36 on 14 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #Alternative age reference\n",
    "# #Prepare age interaction model with National Sleep Foundation Age Groups\n",
    "# Climate_Controls_DF$age_group_NSF <- as.factor(Climate_Controls_DF$age_group_NSF)\n",
    "\n",
    "# #set middle adulthood as comparison\n",
    "# Climate_Controls_DF$age_group_NSF <- relevel(Climate_Controls_DF$age_group_NSF, ref=\"age65_above_older\")\n",
    "# #levels(Climate_Controls_DF$age_group_NSF)\n",
    "\n",
    "# linear_t_age <- felm(formula = sleep_duration_minutes ~ tmin + tmin:age_group_NSF + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "# summary(linear_t_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{Marginal Effect of Tmin: Alternative Age Ref Group} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{1}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-2} \n",
      " & Base Level: >65 years \\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " tmin & $-$0.607$^{***}$ \\\\ \n",
      "  & (0.051) \\\\ \n",
      "  tmin\\_norm\\_w7 & $-$0.177$^{**}$ \\\\ \n",
      "  & (0.085) \\\\ \n",
      "  prcp & 0.484$^{***}$ \\\\ \n",
      "  & (0.120) \\\\ \n",
      "  prcp\\_norm\\_w7 & 2.133$^{**}$ \\\\ \n",
      "  & (1.087) \\\\ \n",
      "  trange & $-$0.208$^{***}$ \\\\ \n",
      "  & (0.023) \\\\ \n",
      "  trange\\_norm\\_w7 & 0.059 \\\\ \n",
      "  & (0.147) \\\\ \n",
      "  cloud\\_rean2 & 0.010$^{***}$ \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  cloud\\_norm\\_7\\_rean2 & $-$0.039 \\\\ \n",
      "  & (0.024) \\\\ \n",
      "  rhum\\_rean2 & 0.001 \\\\ \n",
      "  & (0.008) \\\\ \n",
      "  rhum\\_norm\\_7\\_rean2 & 0.104$^{***}$ \\\\ \n",
      "  & (0.040) \\\\ \n",
      "  wind\\_rean2 & 0.128$^{***}$ \\\\ \n",
      "  & (0.022) \\\\ \n",
      "  wind\\_norm\\_7\\_rean2 & $-$0.292 \\\\ \n",
      "  & (0.186) \\\\ \n",
      "  tmin:age\\_group\\_NSFage25\\_64\\_adults & 0.324$^{***}$ \\\\ \n",
      "  & (0.042) \\\\ \n",
      "  tmin:age\\_group\\_NSFage19\\_24\\_young & 0.298$^{***}$ \\\\ \n",
      "  & (0.065) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes \\\\ \n",
      "Date FE & Yes \\\\ \n",
      "State:Month FE & Yes \\\\ \n",
      "Observations & 4,405,171 \\\\ \n",
      "R$^{2}$ & 0.284 \\\\ \n",
      "Adjusted R$^{2}$ & 0.274 \\\\ \n",
      "Residual Std. Error & 77.707 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{1}{r}{Standard errors are in parentheses and are clustered on the first admin level.} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# #Latex output\n",
    "# invisible(stargazer(linear_t_age,\n",
    "#           type='latex',\n",
    "#           title = \"Marginal Effect of Tmin: Alternative Age Ref Group\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Base Level: >65 years'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the first admin level.'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#FIG 3B - Temperature Effects by Sex "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'MALE'</li>\n",
       "\t<li>''</li>\n",
       "\t<li>'FEMALE'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'MALE'\n",
       "\\item ''\n",
       "\\item 'FEMALE'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'MALE'\n",
       "2. ''\n",
       "3. 'FEMALE'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"MALE\"   \"\"       \"FEMALE\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmin + tmin:gender +      tmin_norm_w7 + prcp + prcp_norm_w7 + trange + trange_norm_w7 +      cloud_rean2 + cloud_norm_7_rean2 + rhum_rean2 + rhum_norm_7_rean2 +      wind_rean2 + wind_norm_7_rean2 | userID + unique_day_no +      stateyrmn | 0 | state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-384.38  -49.13   -2.32   45.75  408.08 \n",
       "\n",
       "Coefficients:\n",
       "                     Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin               -0.2729368    0.0291069  -9.377  < 2e-16 ***\n",
       "tmin_norm_w7       -0.1832799    0.0856524  -2.140  0.03237 *  \n",
       "prcp                0.4845424    0.1206621   4.016 5.93e-05 ***\n",
       "prcp_norm_w7        2.1177041    1.0846454   1.952  0.05089 .  \n",
       "trange             -0.2071102    0.0225743  -9.175  < 2e-16 ***\n",
       "trange_norm_w7      0.0566476    0.1470161   0.385  0.70000    \n",
       "cloud_rean2         0.0101671    0.0023751   4.281 1.86e-05 ***\n",
       "cloud_norm_7_rean2 -0.0389737    0.0240173  -1.623  0.10465    \n",
       "rhum_rean2          0.0004628    0.0075249   0.062  0.95096    \n",
       "rhum_norm_7_rean2   0.1028607    0.0396514   2.594  0.00948 ** \n",
       "wind_rean2          0.1283855    0.0218496   5.876 4.21e-09 ***\n",
       "wind_norm_7_rean2  -0.2944513    0.1871516  -1.573  0.11564    \n",
       "tmin:genderFEMALE  -0.0646960    0.0224510  -2.882  0.00396 ** \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.71 on 4345351 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2842   Adjusted R-squared: 0.2744 \n",
       "Multiple R-squared(proj model): 0.0001588   Adjusted R-squared: -0.01361 \n",
       "F-statistic(full model, *iid*):28.84 on 59819 and 4345351 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model):  40.5 on 13 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Estimate</th><th scope=col>Cluster s.e.</th><th scope=col>t value</th><th scope=col>Pr(&gt;|t|)</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>tmin</th><td>-0.2729367567</td><td>0.029106914  </td><td>-9.37704221  </td><td>6.788022e-21 </td></tr>\n",
       "\t<tr><th scope=row>tmin_norm_w7</th><td>-0.1832798716</td><td>0.085652448  </td><td>-2.13980892  </td><td>3.237027e-02 </td></tr>\n",
       "\t<tr><th scope=row>prcp</th><td> 0.4845424068</td><td>0.120662116  </td><td> 4.01569623  </td><td>5.927155e-05 </td></tr>\n",
       "\t<tr><th scope=row>prcp_norm_w7</th><td> 2.1177040608</td><td>1.084645440  </td><td> 1.95243900  </td><td>5.088617e-02 </td></tr>\n",
       "\t<tr><th scope=row>trange</th><td>-0.2071101729</td><td>0.022574290  </td><td>-9.17460414  </td><td>4.534258e-20 </td></tr>\n",
       "\t<tr><th scope=row>trange_norm_w7</th><td> 0.0566475548</td><td>0.147016103  </td><td> 0.38531531  </td><td>7.000038e-01 </td></tr>\n",
       "\t<tr><th scope=row>cloud_rean2</th><td> 0.0101670605</td><td>0.002375067  </td><td> 4.28074746  </td><td>1.862706e-05 </td></tr>\n",
       "\t<tr><th scope=row>cloud_norm_7_rean2</th><td>-0.0389737085</td><td>0.024017279  </td><td>-1.62273619  </td><td>1.046459e-01 </td></tr>\n",
       "\t<tr><th scope=row>rhum_rean2</th><td> 0.0004628271</td><td>0.007524851  </td><td> 0.06150649  </td><td>9.509559e-01 </td></tr>\n",
       "\t<tr><th scope=row>rhum_norm_7_rean2</th><td> 0.1028606942</td><td>0.039651423  </td><td> 2.59412362  </td><td>9.483270e-03 </td></tr>\n",
       "\t<tr><th scope=row>wind_rean2</th><td> 0.1283854992</td><td>0.021849622  </td><td> 5.87586830  </td><td>4.206632e-09 </td></tr>\n",
       "\t<tr><th scope=row>wind_norm_7_rean2</th><td>-0.2944512517</td><td>0.187151647  </td><td>-1.57332974  </td><td>1.156426e-01 </td></tr>\n",
       "\t<tr><th scope=row>tmin:genderFEMALE</th><td>-0.0646960480</td><td>0.022451043  </td><td>-2.88164995  </td><td>3.956009e-03 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & Estimate & Cluster s.e. & t value & Pr(>\\textbar{}t\\textbar{})\\\\\n",
       "\\hline\n",
       "\ttmin & -0.2729367567 & 0.029106914   & -9.37704221   & 6.788022e-21 \\\\\n",
       "\ttmin\\_norm\\_w7 & -0.1832798716 & 0.085652448   & -2.13980892   & 3.237027e-02 \\\\\n",
       "\tprcp &  0.4845424068 & 0.120662116   &  4.01569623   & 5.927155e-05 \\\\\n",
       "\tprcp\\_norm\\_w7 &  2.1177040608 & 1.084645440   &  1.95243900   & 5.088617e-02 \\\\\n",
       "\ttrange & -0.2071101729 & 0.022574290   & -9.17460414   & 4.534258e-20 \\\\\n",
       "\ttrange\\_norm\\_w7 &  0.0566475548 & 0.147016103   &  0.38531531   & 7.000038e-01 \\\\\n",
       "\tcloud\\_rean2 &  0.0101670605 & 0.002375067   &  4.28074746   & 1.862706e-05 \\\\\n",
       "\tcloud\\_norm\\_7\\_rean2 & -0.0389737085 & 0.024017279   & -1.62273619   & 1.046459e-01 \\\\\n",
       "\trhum\\_rean2 &  0.0004628271 & 0.007524851   &  0.06150649   & 9.509559e-01 \\\\\n",
       "\trhum\\_norm\\_7\\_rean2 &  0.1028606942 & 0.039651423   &  2.59412362   & 9.483270e-03 \\\\\n",
       "\twind\\_rean2 &  0.1283854992 & 0.021849622   &  5.87586830   & 4.206632e-09 \\\\\n",
       "\twind\\_norm\\_7\\_rean2 & -0.2944512517 & 0.187151647   & -1.57332974   & 1.156426e-01 \\\\\n",
       "\ttmin:genderFEMALE & -0.0646960480 & 0.022451043   & -2.88164995   & 3.956009e-03 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | Estimate | Cluster s.e. | t value | Pr(>|t|) |\n",
       "|---|---|---|---|---|\n",
       "| tmin | -0.2729367567 | 0.029106914   | -9.37704221   | 6.788022e-21  |\n",
       "| tmin_norm_w7 | -0.1832798716 | 0.085652448   | -2.13980892   | 3.237027e-02  |\n",
       "| prcp |  0.4845424068 | 0.120662116   |  4.01569623   | 5.927155e-05  |\n",
       "| prcp_norm_w7 |  2.1177040608 | 1.084645440   |  1.95243900   | 5.088617e-02  |\n",
       "| trange | -0.2071101729 | 0.022574290   | -9.17460414   | 4.534258e-20  |\n",
       "| trange_norm_w7 |  0.0566475548 | 0.147016103   |  0.38531531   | 7.000038e-01  |\n",
       "| cloud_rean2 |  0.0101670605 | 0.002375067   |  4.28074746   | 1.862706e-05  |\n",
       "| cloud_norm_7_rean2 | -0.0389737085 | 0.024017279   | -1.62273619   | 1.046459e-01  |\n",
       "| rhum_rean2 |  0.0004628271 | 0.007524851   |  0.06150649   | 9.509559e-01  |\n",
       "| rhum_norm_7_rean2 |  0.1028606942 | 0.039651423   |  2.59412362   | 9.483270e-03  |\n",
       "| wind_rean2 |  0.1283854992 | 0.021849622   |  5.87586830   | 4.206632e-09  |\n",
       "| wind_norm_7_rean2 | -0.2944512517 | 0.187151647   | -1.57332974   | 1.156426e-01  |\n",
       "| tmin:genderFEMALE | -0.0646960480 | 0.022451043   | -2.88164995   | 3.956009e-03  |\n",
       "\n"
      ],
      "text/plain": [
       "                   Estimate      Cluster s.e. t value     Pr(>|t|)    \n",
       "tmin               -0.2729367567 0.029106914  -9.37704221 6.788022e-21\n",
       "tmin_norm_w7       -0.1832798716 0.085652448  -2.13980892 3.237027e-02\n",
       "prcp                0.4845424068 0.120662116   4.01569623 5.927155e-05\n",
       "prcp_norm_w7        2.1177040608 1.084645440   1.95243900 5.088617e-02\n",
       "trange             -0.2071101729 0.022574290  -9.17460414 4.534258e-20\n",
       "trange_norm_w7      0.0566475548 0.147016103   0.38531531 7.000038e-01\n",
       "cloud_rean2         0.0101670605 0.002375067   4.28074746 1.862706e-05\n",
       "cloud_norm_7_rean2 -0.0389737085 0.024017279  -1.62273619 1.046459e-01\n",
       "rhum_rean2          0.0004628271 0.007524851   0.06150649 9.509559e-01\n",
       "rhum_norm_7_rean2   0.1028606942 0.039651423   2.59412362 9.483270e-03\n",
       "wind_rean2          0.1283854992 0.021849622   5.87586830 4.206632e-09\n",
       "wind_norm_7_rean2  -0.2944512517 0.187151647  -1.57332974 1.156426e-01\n",
       "tmin:genderFEMALE  -0.0646960480 0.022451043  -2.88164995 3.956009e-03"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Climate_Controls_DF$gender <- relevel(Climate_Controls_DF$gender, ref=\"MALE\")\n",
    "# levels(Climate_Controls_DF$gender)\n",
    "# linear_t_gen <- felm(formula = sleep_duration_minutes ~ tmin + tmin:gender + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "# summary(linear_t_gen)\n",
    "\n",
    "# #save model coefficients for plotting\n",
    "# dt <- as.data.frame(summary(linear_t_gen)$coefficients[, 1:4]) # extract from felm summary (use df to keep coefficient\n",
    "# write.csv(dt,file=\"model_data/Fig_3B_linear_tmin_subgroup_by_sex.csv\")#save data as CV\n",
    "# dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Subgroups</th><th scope=col>Estimate</th><th scope=col>Cluster.s.e.</th><th scope=col>t.value</th><th scope=col>Pr...t..</th><th scope=col>base</th><th scope=col>plotcoef</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Male        </td><td>-0.27293676 </td><td>0.02910691  </td><td>-9.377042   </td><td>6.788022e-21</td><td>-0.2729368  </td><td>0.2729368   </td></tr>\n",
       "\t<tr><td>Female      </td><td>-0.06469605 </td><td>0.02245104  </td><td>-2.881650   </td><td>3.956009e-03</td><td>-0.2729368  </td><td>0.3376328   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllll}\n",
       " Subgroups & Estimate & Cluster.s.e. & t.value & Pr...t.. & base & plotcoef\\\\\n",
       "\\hline\n",
       "\t Male         & -0.27293676  & 0.02910691   & -9.377042    & 6.788022e-21 & -0.2729368   & 0.2729368   \\\\\n",
       "\t Female       & -0.06469605  & 0.02245104   & -2.881650    & 3.956009e-03 & -0.2729368   & 0.3376328   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Subgroups | Estimate | Cluster.s.e. | t.value | Pr...t.. | base | plotcoef |\n",
       "|---|---|---|---|---|---|---|\n",
       "| Male         | -0.27293676  | 0.02910691   | -9.377042    | 6.788022e-21 | -0.2729368   | 0.2729368    |\n",
       "| Female       | -0.06469605  | 0.02245104   | -2.881650    | 3.956009e-03 | -0.2729368   | 0.3376328    |\n",
       "\n"
      ],
      "text/plain": [
       "  Subgroups Estimate    Cluster.s.e. t.value   Pr...t..     base      \n",
       "1 Male      -0.27293676 0.02910691   -9.377042 6.788022e-21 -0.2729368\n",
       "2 Female    -0.06469605 0.02245104   -2.881650 3.956009e-03 -0.2729368\n",
       "  plotcoef \n",
       "1 0.2729368\n",
       "2 0.3376328"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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KGJdfY3DgBAWbF4PF7dczhs0tPT27Vr98EHH1T3RAAAqsGRco4dAACHSNgBAARC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\n8RvxZ0mdFQ+yTAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#load dt\n",
    "dt <- read.csv(file=\"model_data/Fig_3B_linear_tmin_subgroup_by_sex.csv\",header=T)\n",
    "#Plot tmin results\n",
    "dt <- dt[grepl(\"tmin\", dt$X), ] # extract variable name (e.g., tmax.cut, ppt.cut, ...), limit to matches\n",
    "dt <- dt[!grepl(\"norm\", dt$X), ] #deselect norm variables\n",
    "dt <- as.data.table(dt)\n",
    "#recover marginal effects \n",
    "base<-dt[X==\"tmin\",.(Estimate)]\n",
    "dt[,base:=base]\n",
    "base<-dt[X==\"tmin\",.(Estimate)]\n",
    "dt[,plotcoef:=Estimate]\n",
    "dt[X!=\"tmin\",plotcoef:=plotcoef+base]\n",
    "dt[,plotcoef:=abs(plotcoef)] #plot absolute sleep loss\n",
    "#specify names for plotting\n",
    "dt[X==\"tmin\",X:=\"Male\"]\n",
    "dt[X==\"tmin:genderFEMALE\",X:=\"Female\"]\n",
    "setnames(dt,\"X\",\"Subgroups\")\n",
    "dt[,Subgroups:=as.factor(Subgroups)]\n",
    "dt\n",
    "barplot<-ggplot(dt) +\n",
    "  geom_bar(aes(x=Subgroups, y=plotcoef), stat=\"identity\", fill=\"#ffb600\", alpha=1) +\n",
    "  geom_errorbar(aes(x=Subgroups, ymin=plotcoef-Cluster.s.e.*1.96, ymax=plotcoef+Cluster.s.e.*1.96), width=0, alpha=0.9, size=1) +\n",
    "  scale_y_continuous(breaks=seq(0,1.5,0.5),limits=c(0,1.5),labels = scales::number_format(accuracy = 0.01)) +\n",
    "  theme_classic() +\n",
    "  ggtitle(\"Marginal Effect of Tmin by sex\")\n",
    "barplot\n",
    "#ggsave(\"barplot_marginal_effect_by_sex.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{Marginal Effect of Tmin: Sex} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{1}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-2} \n",
      " & Base Level: Male \\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " tmin & $-$0.273$^{***}$ \\\\ \n",
      "  & (0.029) \\\\ \n",
      "  tmin\\_norm\\_w7 & $-$0.183$^{**}$ \\\\ \n",
      "  & (0.086) \\\\ \n",
      "  prcp & 0.485$^{***}$ \\\\ \n",
      "  & (0.121) \\\\ \n",
      "  prcp\\_norm\\_w7 & 2.118$^{*}$ \\\\ \n",
      "  & (1.085) \\\\ \n",
      "  trange & $-$0.207$^{***}$ \\\\ \n",
      "  & (0.023) \\\\ \n",
      "  trange\\_norm\\_w7 & 0.057 \\\\ \n",
      "  & (0.147) \\\\ \n",
      "  cloud\\_rean2 & 0.010$^{***}$ \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  cloud\\_norm\\_7\\_rean2 & $-$0.039 \\\\ \n",
      "  & (0.024) \\\\ \n",
      "  rhum\\_rean2 & 0.0005 \\\\ \n",
      "  & (0.008) \\\\ \n",
      "  rhum\\_norm\\_7\\_rean2 & 0.103$^{***}$ \\\\ \n",
      "  & (0.040) \\\\ \n",
      "  wind\\_rean2 & 0.128$^{***}$ \\\\ \n",
      "  & (0.022) \\\\ \n",
      "  wind\\_norm\\_7\\_rean2 & $-$0.294 \\\\ \n",
      "  & (0.187) \\\\ \n",
      "  tmin:genderFEMALE & $-$0.065$^{***}$ \\\\ \n",
      "  & (0.022) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes \\\\ \n",
      "Date FE & Yes \\\\ \n",
      "State:Month FE & Yes \\\\ \n",
      "Observations & 4,405,171 \\\\ \n",
      "R$^{2}$ & 0.284 \\\\ \n",
      "Adjusted R$^{2}$ & 0.274 \\\\ \n",
      "Residual Std. Error & 77.708 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{1}{r}{Standard errors are in parentheses and are clustered on the first admin level.} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# #Latex output\n",
    "# invisible(stargazer(linear_t_gen,\n",
    "#           type='latex',\n",
    "#           title = \"Marginal Effect of Tmin: Sex\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Base Level: Male'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the first admin level.'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#FIG 3C - Temperature Effects by GNI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmin + tmin:income_cat +      tmin_norm_w7 + prcp + prcp_norm_w7 + trange + trange_norm_w7 +      cloud_rean2 + cloud_norm_7_rean2 + rhum_rean2 + rhum_norm_7_rean2 +      wind_rean2 + wind_norm_7_rean2 | userID + unique_day_no +      stateyrmn | 0 | state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-384.38  -49.12   -2.32   45.76  407.70 \n",
       "\n",
       "Coefficients:\n",
       "                                     Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin                               -0.3024628    0.0290973 -10.395  < 2e-16 ***\n",
       "tmin_norm_w7                       -0.1757590    0.0847832  -2.073  0.03817 *  \n",
       "prcp                                0.4820972    0.1205263   4.000 6.34e-05 ***\n",
       "prcp_norm_w7                        2.1238418    1.0856055   1.956  0.05042 .  \n",
       "trange                             -0.2074072    0.0226457  -9.159  < 2e-16 ***\n",
       "trange_norm_w7                      0.0548613    0.1469892   0.373  0.70897    \n",
       "cloud_rean2                         0.0101664    0.0023857   4.261 2.03e-05 ***\n",
       "cloud_norm_7_rean2                 -0.0389902    0.0239339  -1.629  0.10330    \n",
       "rhum_rean2                          0.0007256    0.0075673   0.096  0.92361    \n",
       "rhum_norm_7_rean2                   0.1043327    0.0394745   2.643  0.00822 ** \n",
       "wind_rean2                          0.1275374    0.0218858   5.827 5.63e-09 ***\n",
       "wind_norm_7_rean2                  -0.2954294    0.1875176  -1.575  0.11515    \n",
       "tmin:income_catUpper middle income  0.0684581    0.0522544   1.310  0.19016    \n",
       "tmin:income_catLower middle income -0.5447023    0.3178922  -1.713  0.08662 .  \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.71 on 4345350 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2842   Adjusted R-squared: 0.2744 \n",
       "Multiple R-squared(proj model): 0.0001542   Adjusted R-squared: -0.01361 \n",
       "F-statistic(full model, *iid*):28.84 on 59820 and 4345350 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 37.09 on 14 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Estimate</th><th scope=col>Cluster s.e.</th><th scope=col>t value</th><th scope=col>Pr(&gt;|t|)</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>tmin</th><td>-0.3024628435</td><td>0.029097350  </td><td>-10.39485891 </td><td>2.618530e-25 </td></tr>\n",
       "\t<tr><th scope=row>tmin_norm_w7</th><td>-0.1757590106</td><td>0.084783197  </td><td> -2.07304062 </td><td>3.816856e-02 </td></tr>\n",
       "\t<tr><th scope=row>prcp</th><td> 0.4820972138</td><td>0.120526306  </td><td>  3.99993353 </td><td>6.336132e-05 </td></tr>\n",
       "\t<tr><th scope=row>prcp_norm_w7</th><td> 2.1238418038</td><td>1.085605502  </td><td>  1.95636610 </td><td>5.042211e-02 </td></tr>\n",
       "\t<tr><th scope=row>trange</th><td>-0.2074072336</td><td>0.022645696  </td><td> -9.15879253 </td><td>5.250299e-20 </td></tr>\n",
       "\t<tr><th scope=row>trange_norm_w7</th><td> 0.0548613466</td><td>0.146989196  </td><td>  0.37323387 </td><td>7.089744e-01 </td></tr>\n",
       "\t<tr><th scope=row>cloud_rean2</th><td> 0.0101664263</td><td>0.002385745  </td><td>  4.26132181 </td><td>2.032256e-05 </td></tr>\n",
       "\t<tr><th scope=row>cloud_norm_7_rean2</th><td>-0.0389901809</td><td>0.023933857  </td><td> -1.62908054 </td><td>1.032960e-01 </td></tr>\n",
       "\t<tr><th scope=row>rhum_rean2</th><td> 0.0007256275</td><td>0.007567287  </td><td>  0.09589006 </td><td>9.236079e-01 </td></tr>\n",
       "\t<tr><th scope=row>rhum_norm_7_rean2</th><td> 0.1043326523</td><td>0.039474499  </td><td>  2.64303930 </td><td>8.216579e-03 </td></tr>\n",
       "\t<tr><th scope=row>wind_rean2</th><td> 0.1275374373</td><td>0.021885812  </td><td>  5.82740252 </td><td>5.630067e-09 </td></tr>\n",
       "\t<tr><th scope=row>wind_norm_7_rean2</th><td>-0.2954294192</td><td>0.187517576  </td><td> -1.57547589 </td><td>1.151467e-01 </td></tr>\n",
       "\t<tr><th scope=row>tmin:income_catUpper middle income</th><td> 0.0684581201</td><td>0.052254374  </td><td>  1.31009359 </td><td>1.901642e-01 </td></tr>\n",
       "\t<tr><th scope=row>tmin:income_catLower middle income</th><td>-0.5447023387</td><td>0.317892248  </td><td> -1.71348104 </td><td>8.662415e-02 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & Estimate & Cluster s.e. & t value & Pr(>\\textbar{}t\\textbar{})\\\\\n",
       "\\hline\n",
       "\ttmin & -0.3024628435 & 0.029097350   & -10.39485891  & 2.618530e-25 \\\\\n",
       "\ttmin\\_norm\\_w7 & -0.1757590106 & 0.084783197   &  -2.07304062  & 3.816856e-02 \\\\\n",
       "\tprcp &  0.4820972138 & 0.120526306   &   3.99993353  & 6.336132e-05 \\\\\n",
       "\tprcp\\_norm\\_w7 &  2.1238418038 & 1.085605502   &   1.95636610  & 5.042211e-02 \\\\\n",
       "\ttrange & -0.2074072336 & 0.022645696   &  -9.15879253  & 5.250299e-20 \\\\\n",
       "\ttrange\\_norm\\_w7 &  0.0548613466 & 0.146989196   &   0.37323387  & 7.089744e-01 \\\\\n",
       "\tcloud\\_rean2 &  0.0101664263 & 0.002385745   &   4.26132181  & 2.032256e-05 \\\\\n",
       "\tcloud\\_norm\\_7\\_rean2 & -0.0389901809 & 0.023933857   &  -1.62908054  & 1.032960e-01 \\\\\n",
       "\trhum\\_rean2 &  0.0007256275 & 0.007567287   &   0.09589006  & 9.236079e-01 \\\\\n",
       "\trhum\\_norm\\_7\\_rean2 &  0.1043326523 & 0.039474499   &   2.64303930  & 8.216579e-03 \\\\\n",
       "\twind\\_rean2 &  0.1275374373 & 0.021885812   &   5.82740252  & 5.630067e-09 \\\\\n",
       "\twind\\_norm\\_7\\_rean2 & -0.2954294192 & 0.187517576   &  -1.57547589  & 1.151467e-01 \\\\\n",
       "\ttmin:income\\_catUpper middle income &  0.0684581201 & 0.052254374   &   1.31009359  & 1.901642e-01 \\\\\n",
       "\ttmin:income\\_catLower middle income & -0.5447023387 & 0.317892248   &  -1.71348104  & 8.662415e-02 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | Estimate | Cluster s.e. | t value | Pr(>|t|) |\n",
       "|---|---|---|---|---|\n",
       "| tmin | -0.3024628435 | 0.029097350   | -10.39485891  | 2.618530e-25  |\n",
       "| tmin_norm_w7 | -0.1757590106 | 0.084783197   |  -2.07304062  | 3.816856e-02  |\n",
       "| prcp |  0.4820972138 | 0.120526306   |   3.99993353  | 6.336132e-05  |\n",
       "| prcp_norm_w7 |  2.1238418038 | 1.085605502   |   1.95636610  | 5.042211e-02  |\n",
       "| trange | -0.2074072336 | 0.022645696   |  -9.15879253  | 5.250299e-20  |\n",
       "| trange_norm_w7 |  0.0548613466 | 0.146989196   |   0.37323387  | 7.089744e-01  |\n",
       "| cloud_rean2 |  0.0101664263 | 0.002385745   |   4.26132181  | 2.032256e-05  |\n",
       "| cloud_norm_7_rean2 | -0.0389901809 | 0.023933857   |  -1.62908054  | 1.032960e-01  |\n",
       "| rhum_rean2 |  0.0007256275 | 0.007567287   |   0.09589006  | 9.236079e-01  |\n",
       "| rhum_norm_7_rean2 |  0.1043326523 | 0.039474499   |   2.64303930  | 8.216579e-03  |\n",
       "| wind_rean2 |  0.1275374373 | 0.021885812   |   5.82740252  | 5.630067e-09  |\n",
       "| wind_norm_7_rean2 | -0.2954294192 | 0.187517576   |  -1.57547589  | 1.151467e-01  |\n",
       "| tmin:income_catUpper middle income |  0.0684581201 | 0.052254374   |   1.31009359  | 1.901642e-01  |\n",
       "| tmin:income_catLower middle income | -0.5447023387 | 0.317892248   |  -1.71348104  | 8.662415e-02  |\n",
       "\n"
      ],
      "text/plain": [
       "                                   Estimate      Cluster s.e. t value     \n",
       "tmin                               -0.3024628435 0.029097350  -10.39485891\n",
       "tmin_norm_w7                       -0.1757590106 0.084783197   -2.07304062\n",
       "prcp                                0.4820972138 0.120526306    3.99993353\n",
       "prcp_norm_w7                        2.1238418038 1.085605502    1.95636610\n",
       "trange                             -0.2074072336 0.022645696   -9.15879253\n",
       "trange_norm_w7                      0.0548613466 0.146989196    0.37323387\n",
       "cloud_rean2                         0.0101664263 0.002385745    4.26132181\n",
       "cloud_norm_7_rean2                 -0.0389901809 0.023933857   -1.62908054\n",
       "rhum_rean2                          0.0007256275 0.007567287    0.09589006\n",
       "rhum_norm_7_rean2                   0.1043326523 0.039474499    2.64303930\n",
       "wind_rean2                          0.1275374373 0.021885812    5.82740252\n",
       "wind_norm_7_rean2                  -0.2954294192 0.187517576   -1.57547589\n",
       "tmin:income_catUpper middle income  0.0684581201 0.052254374    1.31009359\n",
       "tmin:income_catLower middle income -0.5447023387 0.317892248   -1.71348104\n",
       "                                   Pr(>|t|)    \n",
       "tmin                               2.618530e-25\n",
       "tmin_norm_w7                       3.816856e-02\n",
       "prcp                               6.336132e-05\n",
       "prcp_norm_w7                       5.042211e-02\n",
       "trange                             5.250299e-20\n",
       "trange_norm_w7                     7.089744e-01\n",
       "cloud_rean2                        2.032256e-05\n",
       "cloud_norm_7_rean2                 1.032960e-01\n",
       "rhum_rean2                         9.236079e-01\n",
       "rhum_norm_7_rean2                  8.216579e-03\n",
       "wind_rean2                         5.630067e-09\n",
       "wind_norm_7_rean2                  1.151467e-01\n",
       "tmin:income_catUpper middle income 1.901642e-01\n",
       "tmin:income_catLower middle income 8.662415e-02"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #Prepare GNI interaction model\n",
    "# Climate_Controls_DF$income_cat <- relevel(Climate_Controls_DF$income_cat, ref=\"High income\")\n",
    "# #levels(Climate_Controls_DF$income_cat)\n",
    "\n",
    "# #GNI interaction model\n",
    "# linear_t_GNI <- felm(formula = sleep_duration_minutes ~ tmin + tmin:income_cat + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "# summary(linear_t_GNI)\n",
    "\n",
    "# #save model coefficients for plotting\n",
    "# dt <- as.data.frame(summary(linear_t_GNI)$coefficients[, 1:4]) # extract from felm summary (use df to keep coefficient\n",
    "# write.csv(dt,file=\"model_data/Fig_3B_linear_tmin_subgroup_by_GNI.csv\")#save data as CV\n",
    "# dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Subgroups</th><th scope=col>Estimate</th><th scope=col>Cluster.s.e.</th><th scope=col>t.value</th><th scope=col>Pr...t..</th><th scope=col>base</th><th scope=col>plotcoef</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>3_High Income        </td><td>-0.30246284          </td><td>0.02909735           </td><td>-10.394859           </td><td>2.618530e-25         </td><td>-0.3024628           </td><td>0.3024628            </td></tr>\n",
       "\t<tr><td>1_Lower Middle Income</td><td>-0.54470234          </td><td>0.31789225           </td><td> -1.713481           </td><td>8.662415e-02         </td><td>-0.3024628           </td><td>0.8471652            </td></tr>\n",
       "\t<tr><td>2_Upper Middle Income</td><td> 0.06845812          </td><td>0.05225437           </td><td>  1.310094           </td><td>1.901642e-01         </td><td>-0.3024628           </td><td>0.2340047            </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllll}\n",
       " Subgroups & Estimate & Cluster.s.e. & t.value & Pr...t.. & base & plotcoef\\\\\n",
       "\\hline\n",
       "\t 3\\_High Income         & -0.30246284             & 0.02909735              & -10.394859              & 2.618530e-25            & -0.3024628              & 0.3024628              \\\\\n",
       "\t 1\\_Lower Middle Income & -0.54470234             & 0.31789225              &  -1.713481              & 8.662415e-02            & -0.3024628              & 0.8471652              \\\\\n",
       "\t 2\\_Upper Middle Income &  0.06845812             & 0.05225437              &   1.310094              & 1.901642e-01            & -0.3024628              & 0.2340047              \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Subgroups | Estimate | Cluster.s.e. | t.value | Pr...t.. | base | plotcoef |\n",
       "|---|---|---|---|---|---|---|\n",
       "| 3_High Income         | -0.30246284           | 0.02909735            | -10.394859            | 2.618530e-25          | -0.3024628            | 0.3024628             |\n",
       "| 1_Lower Middle Income | -0.54470234           | 0.31789225            |  -1.713481            | 8.662415e-02          | -0.3024628            | 0.8471652             |\n",
       "| 2_Upper Middle Income |  0.06845812           | 0.05225437            |   1.310094            | 1.901642e-01          | -0.3024628            | 0.2340047             |\n",
       "\n"
      ],
      "text/plain": [
       "  Subgroups             Estimate    Cluster.s.e. t.value    Pr...t..    \n",
       "1 3_High Income         -0.30246284 0.02909735   -10.394859 2.618530e-25\n",
       "2 1_Lower Middle Income -0.54470234 0.31789225    -1.713481 8.662415e-02\n",
       "3 2_Upper Middle Income  0.06845812 0.05225437     1.310094 1.901642e-01\n",
       "  base       plotcoef \n",
       "1 -0.3024628 0.3024628\n",
       "2 -0.3024628 0.8471652\n",
       "3 -0.3024628 0.2340047"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Rl7XC+5YYcOHTp06JC4vGXLlmoZHgDgUHConMeuSZMmNWvWLPl0xfLly1NSUpo1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\nAAAAAKzEvwDGVwd0g2YVcwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#load dt\n",
    "dt <- read.csv(file=\"model_data/Fig_3C_linear_tmin_subgroup_by_GNI.csv\",header=T)\n",
    "#Plot tmin results\n",
    "dt <- dt[grepl(\"tmin\", dt$X), ] # extract variable name (e.g., tmax.cut, ppt.cut, ...), limit to matches\n",
    "dt <- dt[!grepl(\"norm\", dt$X), ] #deselect norm variables\n",
    "dt <- as.data.table(dt)\n",
    "#recover marginal effects \n",
    "base<-dt[X==\"tmin\",.(Estimate)]\n",
    "dt[,base:=base]\n",
    "base<-dt[X==\"tmin\",.(Estimate)]\n",
    "dt[,plotcoef:=Estimate]\n",
    "dt[X!=\"tmin\",plotcoef:=plotcoef+base]\n",
    "dt[,plotcoef:=abs(plotcoef)] #plot absolute sleep loss\n",
    "#specify names for plotting\n",
    "dt[X==\"tmin\",X:=\"3_High Income\"]\n",
    "dt[X==\"tmin:income_catLower middle income\",X:=\"1_Lower Middle Income\"]\n",
    "dt[X==\"tmin:income_catUpper middle income\",X:=\"2_Upper Middle Income\"]\n",
    "setnames(dt,\"X\",\"Subgroups\")\n",
    "dt[,Subgroups:=as.factor(Subgroups)]\n",
    "dt\n",
    "barplot<-ggplot(dt) +\n",
    "  geom_bar(aes(x=Subgroups, y=plotcoef), stat=\"identity\", fill=\"#ffb600\", alpha=1) +\n",
    "  geom_errorbar(aes(x=Subgroups, ymin=plotcoef-Cluster.s.e.*1.96, ymax=plotcoef+Cluster.s.e.*1.96), width=0, alpha=0.9, size=1) +\n",
    "  scale_y_continuous(breaks=seq(0,1.5,0.5),limits=c(0,1.5),labels = scales::number_format(accuracy = 0.01)) +\n",
    "  theme_classic() +\n",
    "  ggtitle(\"Marginal Effect of Tmin by GNI\")\n",
    "barplot\n",
    "#ggsave(\"barplot_marginal_effect_by_GNI.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{Marginal Effect of Tmin: World Bank Gross National Income Category} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{1}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-2} \n",
      " & Base Level: High Income \\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " tmin & $-$0.302$^{***}$ \\\\ \n",
      "  & (0.029) \\\\ \n",
      "  tmin\\_norm\\_w7 & $-$0.176$^{**}$ \\\\ \n",
      "  & (0.085) \\\\ \n",
      "  prcp & 0.482$^{***}$ \\\\ \n",
      "  & (0.121) \\\\ \n",
      "  prcp\\_norm\\_w7 & 2.124$^{*}$ \\\\ \n",
      "  & (1.086) \\\\ \n",
      "  trange & $-$0.207$^{***}$ \\\\ \n",
      "  & (0.023) \\\\ \n",
      "  trange\\_norm\\_w7 & 0.055 \\\\ \n",
      "  & (0.147) \\\\ \n",
      "  cloud\\_rean2 & 0.010$^{***}$ \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  cloud\\_norm\\_7\\_rean2 & $-$0.039 \\\\ \n",
      "  & (0.024) \\\\ \n",
      "  rhum\\_rean2 & 0.001 \\\\ \n",
      "  & (0.008) \\\\ \n",
      "  rhum\\_norm\\_7\\_rean2 & 0.104$^{***}$ \\\\ \n",
      "  & (0.039) \\\\ \n",
      "  wind\\_rean2 & 0.128$^{***}$ \\\\ \n",
      "  & (0.022) \\\\ \n",
      "  wind\\_norm\\_7\\_rean2 & $-$0.295 \\\\ \n",
      "  & (0.188) \\\\ \n",
      "  tmin:income\\_catLower middle income & $-$0.545$^{*}$ \\\\ \n",
      "  & (0.318) \\\\ \n",
      "  tmin:income\\_catUpper middle income & 0.068 \\\\ \n",
      "  & (0.052) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes \\\\ \n",
      "Date FE & Yes \\\\ \n",
      "State:Month FE & Yes \\\\ \n",
      "Observations & 4,405,171 \\\\ \n",
      "R$^{2}$ & 0.284 \\\\ \n",
      "Adjusted R$^{2}$ & 0.274 \\\\ \n",
      "Residual Std. Error & 77.708 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{1}{r}{Standard errors are in parentheses and are clustered on the first admin level.} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# #Latex output\n",
    "# invisible(stargazer(linear_t_GNI,\n",
    "#           type='latex',\n",
    "#           title = \"Marginal Effect of Tmin: World Bank Gross National Income Category\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Base Level: High Income'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the first admin level.'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#FIG 3D - Temperature Effects by Season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'winter'</li>\n",
       "\t<li>'fall'</li>\n",
       "\t<li>'spring'</li>\n",
       "\t<li>'summer'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'winter'\n",
       "\\item 'fall'\n",
       "\\item 'spring'\n",
       "\\item 'summer'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'winter'\n",
       "2. 'fall'\n",
       "3. 'spring'\n",
       "4. 'summer'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"winter\" \"fall\"   \"spring\" \"summer\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #prepare seasons interaction model\n",
    "# Climate_Controls_DF$seasons_2[Climate_Controls_DF$month==\"12\" | Climate_Controls_DF$month==\"1\" | Climate_Controls_DF$month==\"2\"] <- 'winter'\n",
    "# Climate_Controls_DF$seasons_2[Climate_Controls_DF$month==\"3\" | Climate_Controls_DF$month==\"4\" | Climate_Controls_DF$month==\"5\"] <- 'spring'\n",
    "# Climate_Controls_DF$seasons_2[Climate_Controls_DF$month==\"6\" | Climate_Controls_DF$month==\"7\" | Climate_Controls_DF$month==\"8\"] <- 'summer'\n",
    "# Climate_Controls_DF$seasons_2[Climate_Controls_DF$month==\"9\" | Climate_Controls_DF$month==\"10\" | Climate_Controls_DF$month==\"11\"] <- 'fall'\n",
    "\n",
    "# #correct seasons for southern hemisphere\n",
    "# Climate_Controls_DF$seasons_global <- Climate_Controls_DF$seasons_2\n",
    "\n",
    "# #Argentina\n",
    "# Climate_Controls_DF$seasons_global[Climate_Controls_DF$country==\"Argentina\" & Climate_Controls_DF$seasons_2==\"winter\"] <- 'summer'\n",
    "# Climate_Controls_DF$seasons_global[Climate_Controls_DF$country==\"Argentina\" & Climate_Controls_DF$seasons_2==\"summer\"] <- 'winter'\n",
    "# Climate_Controls_DF$seasons_global[Climate_Controls_DF$country==\"Argentina\" & Climate_Controls_DF$seasons_2==\"fall\"] <- 'spring'\n",
    "# Climate_Controls_DF$seasons_global[Climate_Controls_DF$country==\"Argentina\" & Climate_Controls_DF$seasons_2==\"spring\"] <- 'fall'\n",
    "\n",
    "# #Australia\n",
    "# Climate_Controls_DF$seasons_global[Climate_Controls_DF$country==\"Australia\" & Climate_Controls_DF$seasons_2==\"winter\"] <- 'summer'\n",
    "# Climate_Controls_DF$seasons_global[Climate_Controls_DF$country==\"Australia\" & Climate_Controls_DF$seasons_2==\"summer\"] <- 'winter'\n",
    "# Climate_Controls_DF$seasons_global[Climate_Controls_DF$country==\"Australia\" & Climate_Controls_DF$seasons_2==\"fall\"] <- 'spring'\n",
    "# Climate_Controls_DF$seasons_global[Climate_Controls_DF$country==\"Australia\" & Climate_Controls_DF$seasons_2==\"spring\"] <- 'fall'\n",
    "\n",
    "# #Chile\n",
    "# Climate_Controls_DF$seasons_global[Climate_Controls_DF$country==\"Chile\" & Climate_Controls_DF$seasons_2==\"winter\"] <- 'summer'\n",
    "# Climate_Controls_DF$seasons_global[Climate_Controls_DF$country==\"Chile\" & Climate_Controls_DF$seasons_2==\"summer\"] <- 'winter'\n",
    "# Climate_Controls_DF$seasons_global[Climate_Controls_DF$country==\"Chile\" & Climate_Controls_DF$seasons_2==\"fall\"] <- 'spring'\n",
    "# Climate_Controls_DF$seasons_global[Climate_Controls_DF$country==\"Chile\" & Climate_Controls_DF$seasons_2==\"spring\"] <- 'fall'\n",
    "\n",
    "# #Indonesia\n",
    "# Climate_Controls_DF$seasons_global[Climate_Controls_DF$country==\"Indonesia\" & Climate_Controls_DF$seasons_2==\"winter\"] <- 'summer'\n",
    "# Climate_Controls_DF$seasons_global[Climate_Controls_DF$country==\"Indonesia\" & Climate_Controls_DF$seasons_2==\"summer\"] <- 'winter'\n",
    "# Climate_Controls_DF$seasons_global[Climate_Controls_DF$country==\"Indonesia\" & Climate_Controls_DF$seasons_2==\"fall\"] <- 'spring'\n",
    "# Climate_Controls_DF$seasons_global[Climate_Controls_DF$country==\"Indonesia\" & Climate_Controls_DF$seasons_2==\"spring\"] <- 'fall'\n",
    "\n",
    "# #New Zealand\n",
    "# Climate_Controls_DF$seasons_global[Climate_Controls_DF$country==\"New Zealand\" & Climate_Controls_DF$seasons_2==\"winter\"] <- 'summer'\n",
    "# Climate_Controls_DF$seasons_global[Climate_Controls_DF$country==\"New Zealand\" & Climate_Controls_DF$seasons_2==\"summer\"] <- 'winter'\n",
    "# Climate_Controls_DF$seasons_global[Climate_Controls_DF$country==\"New Zealand\" & Climate_Controls_DF$seasons_2==\"fall\"] <- 'spring'\n",
    "# Climate_Controls_DF$seasons_global[Climate_Controls_DF$country==\"New Zealand\" & Climate_Controls_DF$seasons_2==\"spring\"] <- 'fall'\n",
    "\n",
    "# #South Africa\n",
    "# Climate_Controls_DF$seasons_global[Climate_Controls_DF$country==\"South Africa\" & Climate_Controls_DF$seasons_2==\"winter\"] <- 'summer'\n",
    "# Climate_Controls_DF$seasons_global[Climate_Controls_DF$country==\"South Africa\" & Climate_Controls_DF$seasons_2==\"summer\"] <- 'winter'\n",
    "# Climate_Controls_DF$seasons_global[Climate_Controls_DF$country==\"South Africa\" & Climate_Controls_DF$seasons_2==\"fall\"] <- 'spring'\n",
    "# Climate_Controls_DF$seasons_global[Climate_Controls_DF$country==\"South Africa\" & Climate_Controls_DF$seasons_2==\"spring\"] <- 'fall'\n",
    "\n",
    "# #set as factor\n",
    "# Climate_Controls_DF$seasons_global <- as.factor(Climate_Controls_DF$seasons_global)\n",
    "# #update to correct global seasons for north and southern hemisphere\n",
    "# Climate_Controls_DF$seasons <- Climate_Controls_DF$seasons_global\n",
    "\n",
    "# #set winter as comparison\n",
    "# #drop unused levels\n",
    "# Climate_Controls_DF$seasons <- factor(Climate_Controls_DF$seasons)\n",
    "# Climate_Controls_DF$seasons <- relevel(Climate_Controls_DF$seasons, ref=\"winter\")\n",
    "# levels(Climate_Controls_DF$seasons)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmin + tmin:seasons +      tmin_norm_w7 + prcp + prcp_norm_w7 + trange + trange_norm_w7 +      cloud_rean2 + cloud_norm_7_rean2 + rhum_rean2 + rhum_norm_7_rean2 +      wind_rean2 + wind_norm_7_rean2 | userID + unique_day_no +      stateyrmn | 0 | state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-384.26  -49.13   -2.32   45.76  407.69 \n",
       "\n",
       "Coefficients:\n",
       "                     Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin               -1.994e-01    4.190e-02  -4.759 1.95e-06 ***\n",
       "tmin_norm_w7       -1.895e-01    8.632e-02  -2.196  0.02811 *  \n",
       "prcp                4.580e-01    1.171e-01   3.912 9.16e-05 ***\n",
       "prcp_norm_w7        2.275e+00    1.128e+00   2.016  0.04380 *  \n",
       "trange             -2.059e-01    2.277e-02  -9.043  < 2e-16 ***\n",
       "trange_norm_w7      2.722e-02    1.473e-01   0.185  0.85343    \n",
       "cloud_rean2         1.008e-02    2.386e-03   4.226 2.38e-05 ***\n",
       "cloud_norm_7_rean2 -3.830e-02    2.375e-02  -1.613  0.10676    \n",
       "rhum_rean2          6.513e-06    7.551e-03   0.001  0.99931    \n",
       "rhum_norm_7_rean2   1.036e-01    3.968e-02   2.612  0.00900 ** \n",
       "wind_rean2          1.202e-01    2.141e-02   5.612 2.00e-08 ***\n",
       "wind_norm_7_rean2  -2.865e-01    1.954e-01  -1.467  0.14248    \n",
       "tmin:seasonsfall   -1.539e-01    5.635e-02  -2.731  0.00632 ** \n",
       "tmin:seasonsspring -5.492e-02    4.372e-02  -1.256  0.20906    \n",
       "tmin:seasonssummer -3.499e-01    6.960e-02  -5.027 4.97e-07 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.71 on 4345349 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2842   Adjusted R-squared: 0.2744 \n",
       "Multiple R-squared(proj model): 0.0001697   Adjusted R-squared: -0.01359 \n",
       "F-statistic(full model, *iid*):28.84 on 59821 and 4345349 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 37.19 on 15 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #seasons interaction model\n",
    "# linear_t_seasons <- felm(formula = sleep_duration_minutes ~ tmin + tmin:seasons + tmin_norm_w7 +\n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state,\n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(linear_t_seasons)\n",
    "\n",
    "# #save model coefficients for plotting\n",
    "# dt <- as.data.frame(summary(linear_t_seasons)$coefficients[, 1:4]) # extract from felm summary (use df to keep coefficient\n",
    "# write.csv(dt,file=\"model_data/Fig_3D_linear_tmin_subgroup_by_season.csv\")#save data as CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Subgroups</th><th scope=col>Estimate</th><th scope=col>Cluster.s.e.</th><th scope=col>t.value</th><th scope=col>Pr...t..</th><th scope=col>base</th><th scope=col>plotcoef</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>4_Winter    </td><td>-0.19937289 </td><td>0.04189751  </td><td>-4.758586   </td><td>1.949599e-06</td><td>-0.1993729  </td><td>0.1993729   </td></tr>\n",
       "\t<tr><td>3_Fall      </td><td>-0.15385750 </td><td>0.05634526  </td><td>-2.730620   </td><td>6.321552e-03</td><td>-0.1993729  </td><td>0.3532304   </td></tr>\n",
       "\t<tr><td>1_Spring    </td><td>-0.05491638 </td><td>0.04371740  </td><td>-1.256168   </td><td>2.090553e-01</td><td>-0.1993729  </td><td>0.2542893   </td></tr>\n",
       "\t<tr><td>2_Summer    </td><td>-0.34990444 </td><td>0.06959853  </td><td>-5.027469   </td><td>4.970151e-07</td><td>-0.1993729  </td><td>0.5492773   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllll}\n",
       " Subgroups & Estimate & Cluster.s.e. & t.value & Pr...t.. & base & plotcoef\\\\\n",
       "\\hline\n",
       "\t 4\\_Winter   & -0.19937289  & 0.04189751   & -4.758586    & 1.949599e-06 & -0.1993729   & 0.1993729   \\\\\n",
       "\t 3\\_Fall     & -0.15385750  & 0.05634526   & -2.730620    & 6.321552e-03 & -0.1993729   & 0.3532304   \\\\\n",
       "\t 1\\_Spring   & -0.05491638  & 0.04371740   & -1.256168    & 2.090553e-01 & -0.1993729   & 0.2542893   \\\\\n",
       "\t 2\\_Summer   & -0.34990444  & 0.06959853   & -5.027469    & 4.970151e-07 & -0.1993729   & 0.5492773   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Subgroups | Estimate | Cluster.s.e. | t.value | Pr...t.. | base | plotcoef |\n",
       "|---|---|---|---|---|---|---|\n",
       "| 4_Winter     | -0.19937289  | 0.04189751   | -4.758586    | 1.949599e-06 | -0.1993729   | 0.1993729    |\n",
       "| 3_Fall       | -0.15385750  | 0.05634526   | -2.730620    | 6.321552e-03 | -0.1993729   | 0.3532304    |\n",
       "| 1_Spring     | -0.05491638  | 0.04371740   | -1.256168    | 2.090553e-01 | -0.1993729   | 0.2542893    |\n",
       "| 2_Summer     | -0.34990444  | 0.06959853   | -5.027469    | 4.970151e-07 | -0.1993729   | 0.5492773    |\n",
       "\n"
      ],
      "text/plain": [
       "  Subgroups Estimate    Cluster.s.e. t.value   Pr...t..     base      \n",
       "1 4_Winter  -0.19937289 0.04189751   -4.758586 1.949599e-06 -0.1993729\n",
       "2 3_Fall    -0.15385750 0.05634526   -2.730620 6.321552e-03 -0.1993729\n",
       "3 1_Spring  -0.05491638 0.04371740   -1.256168 2.090553e-01 -0.1993729\n",
       "4 2_Summer  -0.34990444 0.06959853   -5.027469 4.970151e-07 -0.1993729\n",
       "  plotcoef \n",
       "1 0.1993729\n",
       "2 0.3532304\n",
       "3 0.2542893\n",
       "4 0.5492773"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vJKS0tXrlxZt27dvY536tQpHpk3b94nn3wS3z7yyCMr+X0AANRcVXqMXQU2btyY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\nNAb6AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#load dt\n",
    "dt <- read.csv(file=\"model_data/Fig_3D_linear_tmin_subgroup_by_season.csv\",header=T)\n",
    "#Plot tmin results\n",
    "dt <- dt[grepl(\"tmin\", dt$X), ] # extract variable name (e.g., tmax.cut, ppt.cut, ...), limit to matches\n",
    "dt <- dt[!grepl(\"norm\", dt$X), ] #deselect norm variables\n",
    "dt <- as.data.table(dt)\n",
    "#recover marginal effects \n",
    "base<-dt[X==\"tmin\",.(Estimate)]\n",
    "dt[,base:=base]\n",
    "base<-dt[X==\"tmin\",.(Estimate)]\n",
    "dt[,plotcoef:=Estimate]\n",
    "dt[X!=\"tmin\",plotcoef:=plotcoef+base]\n",
    "dt[,plotcoef:=abs(plotcoef)] #plot absolute sleep loss\n",
    "#specify names for plotting\n",
    "dt[X==\"tmin\",X:=\"4_Winter\"]\n",
    "dt[X==\"tmin:seasonsspring\",X:=\"1_Spring\"]\n",
    "dt[X==\"tmin:seasonssummer\",X:=\"2_Summer\"]\n",
    "dt[X==\"tmin:seasonsfall\",X:=\"3_Fall\"]\n",
    "setnames(dt,\"X\",\"Subgroups\")\n",
    "dt[,Subgroups:=as.factor(Subgroups)]\n",
    "dt\n",
    "barplot<-ggplot(dt) +\n",
    "  geom_bar(aes(x=Subgroups, y=plotcoef), stat=\"identity\", fill=\"#ffb600\", alpha=1) +\n",
    "  geom_errorbar(aes(x=Subgroups, ymin=plotcoef-Cluster.s.e.*1.96, ymax=plotcoef+Cluster.s.e.*1.96), width=0, alpha=0.9, size=1) +\n",
    "  scale_y_continuous(breaks=seq(0,1.5,0.5),limits=c(0,1.5),labels = scales::number_format(accuracy = 0.01)) +\n",
    "  theme_classic() +\n",
    "  ggtitle(\"Marginal Effect of Tmin by Season\")\n",
    "barplot\n",
    "#ggsave(\"barplot_marginal_effect_by_Season.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{S6. Nighttime minimum temperature anomalies and sleep duration: Seasons Interaction Model} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{1}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-2} \n",
      " & Base Level: Winter \\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " TMIN & $-$0.199$^{***}$ \\\\ \n",
      "  & (0.042) \\\\ \n",
      "  TMIN_NORM & $-$0.190$^{**}$ \\\\ \n",
      "  & (0.086) \\\\ \n",
      "  PRCP & 0.458$^{***}$ \\\\ \n",
      "  & (0.117) \\\\ \n",
      "  PRCP.NORM & 2.275$^{**}$ \\\\ \n",
      "  & (1.128) \\\\ \n",
      "  TRANGE & $-$0.206$^{***}$ \\\\ \n",
      "  & (0.023) \\\\ \n",
      "  TRANGE.NORM & 0.027 \\\\ \n",
      "  & (0.147) \\\\ \n",
      "  CLOUD & 0.010$^{***}$ \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  CLOUD.NORM & $-$0.038 \\\\ \n",
      "  & (0.024) \\\\ \n",
      "  HUMID & 0.00001 \\\\ \n",
      "  & (0.008) \\\\ \n",
      "  HUMID.NORM & 0.104$^{***}$ \\\\ \n",
      "  & (0.040) \\\\ \n",
      "  WIND & 0.120$^{***}$ \\\\ \n",
      "  & (0.021) \\\\ \n",
      "  WIND.NORM & $-$0.287 \\\\ \n",
      "  & (0.195) \\\\ \n",
      "  Fall TMIN & $-$0.154$^{***}$ \\\\ \n",
      "  & (0.056) \\\\ \n",
      "  Spring TMIN & $-$0.055 \\\\ \n",
      "  & (0.044) \\\\ \n",
      "  Summer TMIN & $-$0.350$^{***}$ \\\\ \n",
      "  & (0.070) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes \\\\ \n",
      "Date FE & Yes \\\\ \n",
      "State:Month FE & Yes \\\\ \n",
      "Observations & 4,405,171 \\\\ \n",
      "R$^{2}$ & 0.284 \\\\ \n",
      "Adjusted R$^{2}$ & 0.274 \\\\ \n",
      "Residual Std. Error & 77.707 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{1}{r}{Standard errors are in parentheses and are clustered on the state admin level} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# #seasons interaction model\n",
    "# invisible(stargazer(linear_t_seasons,\n",
    "#           type='latex',\n",
    "#           title = \"Nighttime minimum temperature and sleep duration: Seasons Interaction Model\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Base Level: Winter'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           covariate.labels = c(\"TMIN\", \"TMIN_NORM\", \"PRCP\", \"PRCP.NORM\", \"TRANGE\", \"TRANGE.NORM\", \"CLOUD\", \"CLOUD.NORM\", \"HUMID\", \"HUMID.NORM\", \"WIND\", \"WIND.NORM\", \"Fall TMIN\", \"Spring TMIN\", \"Summer TMIN\"),\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the state admin level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#FIG 3E - Temperature Effects by Avg. Local 2015-2017 Minimum Temperature Decile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>10%</dt>\n",
       "\t\t<dd>4.07527148949841</dd>\n",
       "\t<dt>20%</dt>\n",
       "\t\t<dd>5.91296039301345</dd>\n",
       "\t<dt>30%</dt>\n",
       "\t\t<dd>7.13233006953655</dd>\n",
       "\t<dt>40%</dt>\n",
       "\t\t<dd>8.27018341957754</dd>\n",
       "\t<dt>50%</dt>\n",
       "\t\t<dd>9.2715147794282</dd>\n",
       "\t<dt>60%</dt>\n",
       "\t\t<dd>10.8251354969331</dd>\n",
       "\t<dt>70%</dt>\n",
       "\t\t<dd>11.0451615133536</dd>\n",
       "\t<dt>80%</dt>\n",
       "\t\t<dd>11.5756348963835</dd>\n",
       "\t<dt>90%</dt>\n",
       "\t\t<dd>15.2412048775681</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[10\\textbackslash{}\\%] 4.07527148949841\n",
       "\\item[20\\textbackslash{}\\%] 5.91296039301345\n",
       "\\item[30\\textbackslash{}\\%] 7.13233006953655\n",
       "\\item[40\\textbackslash{}\\%] 8.27018341957754\n",
       "\\item[50\\textbackslash{}\\%] 9.2715147794282\n",
       "\\item[60\\textbackslash{}\\%] 10.8251354969331\n",
       "\\item[70\\textbackslash{}\\%] 11.0451615133536\n",
       "\\item[80\\textbackslash{}\\%] 11.5756348963835\n",
       "\\item[90\\textbackslash{}\\%] 15.2412048775681\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "10%\n",
       ":   4.0752714894984120%\n",
       ":   5.9129603930134530%\n",
       ":   7.1323300695365540%\n",
       ":   8.2701834195775450%\n",
       ":   9.271514779428260%\n",
       ":   10.825135496933170%\n",
       ":   11.045161513353680%\n",
       ":   11.575634896383590%\n",
       ":   15.2412048775681\n",
       "\n"
      ],
      "text/plain": [
       "      10%       20%       30%       40%       50%       60%       70%       80% \n",
       " 4.075271  5.912960  7.132330  8.270183  9.271515 10.825135 11.045162 11.575635 \n",
       "      90% \n",
       "15.241205 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# quantile(cli_deciles$tmin_2015_17,probs=c(.1,.2,.3,.4,.5,.6,.7,.8,.9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'1'</li>\n",
       "\t<li>'2'</li>\n",
       "\t<li>'3'</li>\n",
       "\t<li>'4'</li>\n",
       "\t<li>'5'</li>\n",
       "\t<li>'6'</li>\n",
       "\t<li>'7'</li>\n",
       "\t<li>'8'</li>\n",
       "\t<li>'9'</li>\n",
       "\t<li>'ten'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item '1'\n",
       "\\item '2'\n",
       "\\item '3'\n",
       "\\item '4'\n",
       "\\item '5'\n",
       "\\item '6'\n",
       "\\item '7'\n",
       "\\item '8'\n",
       "\\item '9'\n",
       "\\item 'ten'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. '1'\n",
       "2. '2'\n",
       "3. '3'\n",
       "4. '4'\n",
       "5. '5'\n",
       "6. '6'\n",
       "7. '7'\n",
       "8. '8'\n",
       "9. '9'\n",
       "10. 'ten'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"1\"   \"2\"   \"3\"   \"4\"   \"5\"   \"6\"   \"7\"   \"8\"   \"9\"   \"ten\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #create climate decile variable\n",
    "# Climate_Controls_DF[,climate_decile:=\"\"]\n",
    "# Climate_Controls_DF[tmin_2015_17<4.07527148949841,climate_decile:=\"1\"]\n",
    "# Climate_Controls_DF[tmin_2015_17>=4.07527148949841&tmin_2015_17<5.91296039301345,climate_decile:=\"2\"]\n",
    "# Climate_Controls_DF[tmin_2015_17>=5.91296039301345&tmin_2015_17<7.13233006953655,climate_decile:=\"3\"]\n",
    "# Climate_Controls_DF[tmin_2015_17>=7.13233006953655&tmin_2015_17<8.27018341957754,climate_decile:=\"4\"]\n",
    "# Climate_Controls_DF[tmin_2015_17>=8.27018341957754&tmin_2015_17<9.2715147794282,climate_decile:=\"5\"]\n",
    "# Climate_Controls_DF[tmin_2015_17>=9.2715147794282&tmin_2015_17<10.8251354969331,climate_decile:=\"6\"]\n",
    "# Climate_Controls_DF[tmin_2015_17>=10.8251354969331&tmin_2015_17<11.0451615133536,climate_decile:=\"7\"]\n",
    "# Climate_Controls_DF[tmin_2015_17>=11.0451615133536&tmin_2015_17<11.5756348963835,climate_decile:=\"8\"]\n",
    "# Climate_Controls_DF[tmin_2015_17>=11.5756348963835&tmin_2015_17<15.2412048775681,climate_decile:=\"9\"]\n",
    "# Climate_Controls_DF[tmin_2015_17>=15.2412048775681,climate_decile:=\"ten\"]\n",
    "# Climate_Controls_DF[,climate_decile:=as.factor(climate_decile)]\n",
    "# levels(Climate_Controls_DF$climate_decile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmin + tmin:climate_decile +      tmin_norm_w7 + prcp + prcp_norm_w7 + trange + trange_norm_w7 +      cloud_rean2 + cloud_norm_7_rean2 + rhum_rean2 + rhum_norm_7_rean2 +      wind_rean2 + wind_norm_7_rean2 | userID + unique_day_no +      stateyrmn | 0 | state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-384.30  -49.13   -2.32   45.75  408.10 \n",
       "\n",
       "Coefficients:\n",
       "                        Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin                   -0.182884     0.033786  -5.413 6.20e-08 ***\n",
       "tmin_norm_w7           -0.129886     0.081872  -1.586  0.11264    \n",
       "prcp                    0.470690     0.119462   3.940 8.15e-05 ***\n",
       "prcp_norm_w7            1.927300     1.107271   1.741  0.08176 .  \n",
       "trange                 -0.218247     0.022925  -9.520  < 2e-16 ***\n",
       "trange_norm_w7          0.038217     0.147538   0.259  0.79561    \n",
       "cloud_rean2             0.010003     0.002413   4.146 3.39e-05 ***\n",
       "cloud_norm_7_rean2     -0.040129     0.023218  -1.728  0.08392 .  \n",
       "rhum_rean2              0.001447     0.007557   0.191  0.84817    \n",
       "rhum_norm_7_rean2       0.115596     0.039351   2.938  0.00331 ** \n",
       "wind_rean2              0.119944     0.022015   5.448 5.09e-08 ***\n",
       "wind_norm_7_rean2      -0.329917     0.182836  -1.804  0.07116 .  \n",
       "tmin:climate_decile2    0.049819     0.052808   0.943  0.34548    \n",
       "tmin:climate_decile3   -0.021836     0.038871  -0.562  0.57428    \n",
       "tmin:climate_decile4   -0.167516     0.051741  -3.238  0.00121 ** \n",
       "tmin:climate_decile5   -0.160321     0.053760  -2.982  0.00286 ** \n",
       "tmin:climate_decile6   -0.204818     0.045415  -4.510 6.49e-06 ***\n",
       "tmin:climate_decile7   -0.215000     0.048258  -4.455 8.38e-06 ***\n",
       "tmin:climate_decile8   -0.237889     0.039903  -5.962 2.50e-09 ***\n",
       "tmin:climate_decile9   -0.282914     0.053478  -5.290 1.22e-07 ***\n",
       "tmin:climate_decileten -0.266084     0.063828  -4.169 3.06e-05 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.71 on 4345343 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2842   Adjusted R-squared: 0.2744 \n",
       "Multiple R-squared(proj model): 0.0001763   Adjusted R-squared: -0.01359 \n",
       "F-statistic(full model, *iid*):28.84 on 59827 and 4345343 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 27.48 on 21 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #climate interaction model\n",
    "# linear_t_cli <- felm(formula = sleep_duration_minutes ~ tmin + tmin:climate_decile + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "# summary(linear_t_cli)\n",
    "\n",
    "# #save model coefficients for plotting\n",
    "# dt <- as.data.frame(summary(linear_t_cli)$coefficients[, 1:4]) # extract from felm summary (use df to keep coefficient\n",
    "# write.csv(dt,file=\"model_data/linear_tmin_subgroup_by_avgtmin_decile.csv\")#save data as CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{Marg. Effect by Avg. Tmin Decile} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{1}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-2} \n",
      " & Base Level: Avg. Tmin Decile 1 (Coldest) \\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " tmin & $-$0.183$^{***}$ \\\\ \n",
      "  & (0.034) \\\\ \n",
      "  tmin\\_norm\\_w7 & $-$0.130 \\\\ \n",
      "  & (0.082) \\\\ \n",
      "  prcp & 0.471$^{***}$ \\\\ \n",
      "  & (0.119) \\\\ \n",
      "  prcp\\_norm\\_w7 & 1.927$^{*}$ \\\\ \n",
      "  & (1.107) \\\\ \n",
      "  trange & $-$0.218$^{***}$ \\\\ \n",
      "  & (0.023) \\\\ \n",
      "  trange\\_norm\\_w7 & 0.038 \\\\ \n",
      "  & (0.148) \\\\ \n",
      "  cloud\\_rean2 & 0.010$^{***}$ \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  cloud\\_norm\\_7\\_rean2 & $-$0.040$^{*}$ \\\\ \n",
      "  & (0.023) \\\\ \n",
      "  rhum\\_rean2 & 0.001 \\\\ \n",
      "  & (0.008) \\\\ \n",
      "  rhum\\_norm\\_7\\_rean2 & 0.116$^{***}$ \\\\ \n",
      "  & (0.039) \\\\ \n",
      "  wind\\_rean2 & 0.120$^{***}$ \\\\ \n",
      "  & (0.022) \\\\ \n",
      "  wind\\_norm\\_7\\_rean2 & $-$0.330$^{*}$ \\\\ \n",
      "  & (0.183) \\\\ \n",
      "  tmin:climate\\_decile2 & 0.050 \\\\ \n",
      "  & (0.053) \\\\ \n",
      "  tmin:climate\\_decile3 & $-$0.022 \\\\ \n",
      "  & (0.039) \\\\ \n",
      "  tmin:climate\\_decile4 & $-$0.168$^{***}$ \\\\ \n",
      "  & (0.052) \\\\ \n",
      "  tmin:climate\\_decile5 & $-$0.160$^{***}$ \\\\ \n",
      "  & (0.054) \\\\ \n",
      "  tmin:climate\\_decile6 & $-$0.205$^{***}$ \\\\ \n",
      "  & (0.045) \\\\ \n",
      "  tmin:climate\\_decile7 & $-$0.215$^{***}$ \\\\ \n",
      "  & (0.048) \\\\ \n",
      "  tmin:climate\\_decile8 & $-$0.238$^{***}$ \\\\ \n",
      "  & (0.040) \\\\ \n",
      "  tmin:climate\\_decile9 & $-$0.283$^{***}$ \\\\ \n",
      "  & (0.053) \\\\ \n",
      "  tmin:climate\\_decileten & $-$0.266$^{***}$ \\\\ \n",
      "  & (0.064) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes \\\\ \n",
      "Date FE & Yes \\\\ \n",
      "State:Month FE & Yes \\\\ \n",
      "Observations & 4,405,171 \\\\ \n",
      "R$^{2}$ & 0.284 \\\\ \n",
      "Adjusted R$^{2}$ & 0.274 \\\\ \n",
      "Residual Std. Error & 77.707 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{1}{r}{Standard errors are in parentheses and are clustered on the first admin level} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# #Latex output\n",
    "# invisible(stargazer(linear_t_cli,\n",
    "#           type='latex',\n",
    "#           title = \"Marg. Effect by Avg. Tmin Decile\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Base Level: Avg. Tmin Decile 1 (Coldest)'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the first admin level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Subgroups</th><th scope=col>Estimate</th><th scope=col>Cluster.s.e.</th><th scope=col>t.value</th><th scope=col>Pr...t..</th><th scope=col>base</th><th scope=col>plotcoef</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Tmin_D1_Cold</td><td>-0.18288394 </td><td>0.03378602  </td><td>-5.4130067  </td><td>6.197844e-08</td><td>-0.1828839  </td><td>0.1828839   </td></tr>\n",
       "\t<tr><td>Tmin_D2     </td><td> 0.04981927 </td><td>0.05280832  </td><td> 0.9433980  </td><td>3.454774e-01</td><td>-0.1828839  </td><td>0.1330647   </td></tr>\n",
       "\t<tr><td>Tmin_D3     </td><td>-0.02183591 </td><td>0.03887076  </td><td>-0.5617568  </td><td>5.742818e-01</td><td>-0.1828839  </td><td>0.2047198   </td></tr>\n",
       "\t<tr><td>Tmin_D4     </td><td>-0.16751603 </td><td>0.05174065  </td><td>-3.2376096  </td><td>1.205365e-03</td><td>-0.1828839  </td><td>0.3504000   </td></tr>\n",
       "\t<tr><td>Tmin_D5     </td><td>-0.16032074 </td><td>0.05376012  </td><td>-2.9821498  </td><td>2.862335e-03</td><td>-0.1828839  </td><td>0.3432047   </td></tr>\n",
       "\t<tr><td>Tmin_D6     </td><td>-0.20481798 </td><td>0.04541550  </td><td>-4.5098699  </td><td>6.486908e-06</td><td>-0.1828839  </td><td>0.3877019   </td></tr>\n",
       "\t<tr><td>Tmin_D7     </td><td>-0.21500021 </td><td>0.04825777  </td><td>-4.4552459  </td><td>8.379920e-06</td><td>-0.1828839  </td><td>0.3978841   </td></tr>\n",
       "\t<tr><td>Tmin_D8     </td><td>-0.23788871 </td><td>0.03990276  </td><td>-5.9617104  </td><td>2.496304e-09</td><td>-0.1828839  </td><td>0.4207727   </td></tr>\n",
       "\t<tr><td>Tmin_D9     </td><td>-0.28291395 </td><td>0.05347788  </td><td>-5.2902990  </td><td>1.221224e-07</td><td>-0.1828839  </td><td>0.4657979   </td></tr>\n",
       "\t<tr><td>Tmin_D10_Hot</td><td>-0.26608370 </td><td>0.06382845  </td><td>-4.1687322  </td><td>3.063045e-05</td><td>-0.1828839  </td><td>0.4489676   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllll}\n",
       " Subgroups & Estimate & Cluster.s.e. & t.value & Pr...t.. & base & plotcoef\\\\\n",
       "\\hline\n",
       "\t Tmin\\_D1\\_Cold & -0.18288394      & 0.03378602       & -5.4130067       & 6.197844e-08     & -0.1828839       & 0.1828839       \\\\\n",
       "\t Tmin\\_D2      &  0.04981927    & 0.05280832     &  0.9433980     & 3.454774e-01   & -0.1828839     & 0.1330647     \\\\\n",
       "\t Tmin\\_D3      & -0.02183591    & 0.03887076     & -0.5617568     & 5.742818e-01   & -0.1828839     & 0.2047198     \\\\\n",
       "\t Tmin\\_D4      & -0.16751603    & 0.05174065     & -3.2376096     & 1.205365e-03   & -0.1828839     & 0.3504000     \\\\\n",
       "\t Tmin\\_D5      & -0.16032074    & 0.05376012     & -2.9821498     & 2.862335e-03   & -0.1828839     & 0.3432047     \\\\\n",
       "\t Tmin\\_D6      & -0.20481798    & 0.04541550     & -4.5098699     & 6.486908e-06   & -0.1828839     & 0.3877019     \\\\\n",
       "\t Tmin\\_D7      & -0.21500021    & 0.04825777     & -4.4552459     & 8.379920e-06   & -0.1828839     & 0.3978841     \\\\\n",
       "\t Tmin\\_D8      & -0.23788871    & 0.03990276     & -5.9617104     & 2.496304e-09   & -0.1828839     & 0.4207727     \\\\\n",
       "\t Tmin\\_D9      & -0.28291395    & 0.05347788     & -5.2902990     & 1.221224e-07   & -0.1828839     & 0.4657979     \\\\\n",
       "\t Tmin\\_D10\\_Hot & -0.26608370      & 0.06382845       & -4.1687322       & 3.063045e-05     & -0.1828839       & 0.4489676       \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Subgroups | Estimate | Cluster.s.e. | t.value | Pr...t.. | base | plotcoef |\n",
       "|---|---|---|---|---|---|---|\n",
       "| Tmin_D1_Cold | -0.18288394  | 0.03378602   | -5.4130067   | 6.197844e-08 | -0.1828839   | 0.1828839    |\n",
       "| Tmin_D2      |  0.04981927  | 0.05280832   |  0.9433980   | 3.454774e-01 | -0.1828839   | 0.1330647    |\n",
       "| Tmin_D3      | -0.02183591  | 0.03887076   | -0.5617568   | 5.742818e-01 | -0.1828839   | 0.2047198    |\n",
       "| Tmin_D4      | -0.16751603  | 0.05174065   | -3.2376096   | 1.205365e-03 | -0.1828839   | 0.3504000    |\n",
       "| Tmin_D5      | -0.16032074  | 0.05376012   | -2.9821498   | 2.862335e-03 | -0.1828839   | 0.3432047    |\n",
       "| Tmin_D6      | -0.20481798  | 0.04541550   | -4.5098699   | 6.486908e-06 | -0.1828839   | 0.3877019    |\n",
       "| Tmin_D7      | -0.21500021  | 0.04825777   | -4.4552459   | 8.379920e-06 | -0.1828839   | 0.3978841    |\n",
       "| Tmin_D8      | -0.23788871  | 0.03990276   | -5.9617104   | 2.496304e-09 | -0.1828839   | 0.4207727    |\n",
       "| Tmin_D9      | -0.28291395  | 0.05347788   | -5.2902990   | 1.221224e-07 | -0.1828839   | 0.4657979    |\n",
       "| Tmin_D10_Hot | -0.26608370  | 0.06382845   | -4.1687322   | 3.063045e-05 | -0.1828839   | 0.4489676    |\n",
       "\n"
      ],
      "text/plain": [
       "   Subgroups    Estimate    Cluster.s.e. t.value    Pr...t..     base      \n",
       "1  Tmin_D1_Cold -0.18288394 0.03378602   -5.4130067 6.197844e-08 -0.1828839\n",
       "2  Tmin_D2       0.04981927 0.05280832    0.9433980 3.454774e-01 -0.1828839\n",
       "3  Tmin_D3      -0.02183591 0.03887076   -0.5617568 5.742818e-01 -0.1828839\n",
       "4  Tmin_D4      -0.16751603 0.05174065   -3.2376096 1.205365e-03 -0.1828839\n",
       "5  Tmin_D5      -0.16032074 0.05376012   -2.9821498 2.862335e-03 -0.1828839\n",
       "6  Tmin_D6      -0.20481798 0.04541550   -4.5098699 6.486908e-06 -0.1828839\n",
       "7  Tmin_D7      -0.21500021 0.04825777   -4.4552459 8.379920e-06 -0.1828839\n",
       "8  Tmin_D8      -0.23788871 0.03990276   -5.9617104 2.496304e-09 -0.1828839\n",
       "9  Tmin_D9      -0.28291395 0.05347788   -5.2902990 1.221224e-07 -0.1828839\n",
       "10 Tmin_D10_Hot -0.26608370 0.06382845   -4.1687322 3.063045e-05 -0.1828839\n",
       "   plotcoef \n",
       "1  0.1828839\n",
       "2  0.1330647\n",
       "3  0.2047198\n",
       "4  0.3504000\n",
       "5  0.3432047\n",
       "6  0.3877019\n",
       "7  0.3978841\n",
       "8  0.4207727\n",
       "9  0.4657979\n",
       "10 0.4489676"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving 6.67 x 6.67 in image\n"
     ]
    },
    {
     "data": {
      "image/png": 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fv3z8zMzMvLmzJlSuPGjWOx2OOPP967d++GDRtGUbSv5QAAlJfisJs7d+62bdv6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\ndTqCyMzWhwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#load dt\n",
    "dt <- read.csv(file=\"model_data/Fig_3E_linear_tmin_subgroup_by_avgtmin_decile.csv\",header=T)\n",
    "#Plot tmin results\n",
    "dt <- dt[grepl(\"tmin\", dt$X), ] # extract variable name (e.g., tmax.cut, ppt.cut, ...), limit to matches\n",
    "dt <- dt[!grepl(\"norm\", dt$X), ] #deselect norm variables\n",
    "dt <- as.data.table(dt)\n",
    "#recover marginal effects \n",
    "base<-dt[X==\"tmin\",.(Estimate)]\n",
    "dt[,base:=base]\n",
    "base<-dt[X==\"tmin\",.(Estimate)]\n",
    "dt[,plotcoef:=Estimate]\n",
    "dt[X!=\"tmin\",plotcoef:=plotcoef+base]\n",
    "dt[,plotcoef:=abs(plotcoef)] #plot absolute sleep loss\n",
    "#specify names for plotting\n",
    "dt[X==\"tmin\",X:=\"Tmin_D1_Cold\"]\n",
    "dt[X==\"tmin:climate_decile2\",X:=\"Tmin_D2\"]\n",
    "dt[X==\"tmin:climate_decile3\",X:=\"Tmin_D3\"]\n",
    "dt[X==\"tmin:climate_decile4\",X:=\"Tmin_D4\"]\n",
    "dt[X==\"tmin:climate_decile5\",X:=\"Tmin_D5\"]\n",
    "dt[X==\"tmin:climate_decile6\",X:=\"Tmin_D6\"]\n",
    "dt[X==\"tmin:climate_decile7\",X:=\"Tmin_D7\"]\n",
    "dt[X==\"tmin:climate_decile8\",X:=\"Tmin_D8\"]\n",
    "dt[X==\"tmin:climate_decile9\",X:=\"Tmin_D9\"]\n",
    "dt[X==\"tmin:climate_decileten\",X:=\"Tmin_D10_Hot\"]\n",
    "setnames(dt,\"X\",\"Subgroups\")\n",
    "dt[,Subgroups:=as.factor(Subgroups)]\n",
    "dt\n",
    "barplot<-ggplot(dt) +\n",
    "  geom_bar(aes(x=Subgroups, y=plotcoef), stat=\"identity\", fill=\"#ffb600\", alpha=1) +\n",
    "  geom_errorbar(aes(x=Subgroups, ymin=plotcoef-Cluster.s.e.*1.96, ymax=plotcoef+Cluster.s.e.*1.96), width=0, alpha=0.9, size=1) +\n",
    "  scale_y_continuous(breaks=seq(0,1.5,0.5),limits=c(0,1.5),labels = scales::number_format(accuracy = 0.01)) +\n",
    "  theme_classic() +\n",
    "  ggtitle(\"Marginal Effect of Tmin by Avg. Tmin\")\n",
    "barplot\n",
    "#ggsave(\"barplot_marginal_effect_by_tavg_decile.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Spline Regression Model for Climate Sleep Loss Projections\n",
    "\n",
    "#Spline Regression Model for Climate Short (<7hr.) Sleep Projections\n",
    "\n",
    "#PROJECTIONS - See R Markdown File for Projection Plot Code\n",
    "#FIG 4A - Average Annual Projected Sleep Loss Due to Temperature\n",
    "#FIG 4B - Average Annual Projected Short Sleep Due to Temperature\n",
    "#FIG 5A-D Projected Annual Temp-Attributed Sleep Loss Net of 2010 - World Map\n",
    "#FIG 5E-H Projected Annual Temp-Attributed Short Sleep Net of 2010 - World Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sleep_duration_minutes ~ tminneg20 + tmin10"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #Sleep Duration splined forecast plot (extract coefficient values)\n",
    "\n",
    "# #drop extra columns and remove rows with NA values for demeanlist \n",
    "# sleep <- as.data.table(Climate_Controls_DF)\n",
    "# #sum(is.na(sleep$tmin))\n",
    "# cols <- c(\"sleep_duration_minutes\", \"tmin\", \"userID\", \"stateyrmn\", \"unique_day_no\", \"CUTPRCP\",\n",
    "#              \"CUTCLOUD_rean2\",\"CUTRHUM_rean2\", \"CUTWIND_rean2\",\n",
    "#              \"CUTTRANGE\",\"CUTTMIN.NORM1981_2010\", \"CUTPRCP.NORM1981_2010\", \"CUTTRANGE.NORM1981_2010\", \n",
    "#              \"CUTCLOUD.NORM1981_2010_rean2\", \"CUTRHUM.NORM1981_2010_rean2\", \"CUTWIND.NORM1981_2010_rean2\")\n",
    "# subcols <- c(\"sleep_duration_minutes\", \"tmin\", \"userID\", \"stateyrmn\", \"unique_day_no\", \"cutprcp\",\n",
    "#              \"cutcloud_rean2\",\"cutrhum_rean2\", \"cutwind_rean2\",\n",
    "#              \"cuttrange\",\"cuttmin_norm_w7\", \"cutprcp_norm_w7\", \"cuttrange_norm_w7\", \n",
    "#              \"cutcloud_norm_7_rean2\", \"cutrhum_norm_7_rean2\", \"cutwind_norm_7_rean2\")\n",
    "\n",
    "# setnames(sleep,old=cols,new=subcols)\n",
    "# sleep <- na.omit(sleep, cols = subcols)\n",
    "# sleep <- subset(sleep, select = subcols)\n",
    "\n",
    "# #set knots\n",
    "# k <- c(-20,10)\n",
    "\n",
    "# ## knot for linear splines\n",
    "# for (i in k) {\n",
    "#   sleep <- sleep[, paste0(\"tmin\",i) := tmin - (i)]\n",
    "#   sleep <- sleep[tmin < i, paste0(\"tmin\",i) := 0]\n",
    "# }\n",
    "# setnames(sleep, names(sleep), gsub(\"-\", \"neg\", names(sleep)))\n",
    "# sleep <- subset(sleep, select = -c(tmin))\n",
    "\n",
    "# ## use demeanlist from lfe package to take out fixed effects to be able to use predict.lm\n",
    "# demean <- demeanlist(mtx = sleep[, .SD, .SDcols=(names(sleep) %like% \"sleep_duration_minutes|tmin\") \n",
    "#                                  & !(names(sleep) %like% \"cuttmin_norm_w7\")],\n",
    "#                      fl = list(userID = sleep[, userID],\n",
    "#                                stateyrmn = sleep[, stateyrmn],\n",
    "#                                unique_day_no = sleep[, unique_day_no],\n",
    "#                                cutprcp = sleep[, cutprcp],\n",
    "#                                cutcloud_rean2 = sleep[, cutcloud_rean2],\n",
    "#                                cutrhum_rean2 = sleep[, cutrhum_rean2],\n",
    "#                                cutwind_rean2 = sleep[, cutwind_rean2],\n",
    "#                                cuttrange = sleep[, cuttrange],\n",
    "#                                cuttmin_norm_w7 = sleep[, cuttmin_norm_w7],\n",
    "#                                cutprcp_norm_w7 = sleep[, cutprcp_norm_w7],\n",
    "#                                cuttrange_norm_w7 = sleep[, cuttrange_norm_w7],\n",
    "#                                cutcloud_norm_7_rean2 = sleep[, cutcloud_norm_7_rean2],\n",
    "#                                cutrhum_norm_7_rean2 = sleep[, cutrhum_norm_7_rean2],\n",
    "#                                cutwind_norm_7_rean2 = sleep[, cutwind_norm_7_rean2]), \n",
    "#                      progress = 5)\n",
    "                     \n",
    "# ## conduct the regression on the demeaned data\n",
    "# cov <- grep(\"tmin\", names(demean), value=TRUE)\n",
    "# cov <- paste0(cov, collapse=\" + \")\n",
    "# fmla <- as.formula(paste(\"sleep_duration_minutes\", \"~\", cov))\n",
    "# fmla\n",
    "# history <- lm(fmla, data=demean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = fmla, data = demean)\n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-383.49  -49.12   -2.31   45.76  408.07 \n",
       "\n",
       "Coefficients:\n",
       "              Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)  4.010e-14  3.677e-02   0.000        1    \n",
       "tminneg20   -1.072e-01  1.912e-02  -5.606 2.07e-08 ***\n",
       "tmin10      -6.184e-01  3.528e-02 -17.529  < 2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.18 on 4405168 degrees of freedom\n",
       "Multiple R-squared:  0.0001417,\tAdjusted R-squared:  0.0001412 \n",
       "F-statistic:   312 on 2 and 4405168 DF,  p-value: < 2.2e-16\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#summary(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{Spline Regression Model for Climate Sleep Loss Projections} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{1}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-2} \n",
      "\\hline \\\\[-1.8ex] \n",
      " tminneg20 & $-$0.107$^{***}$ \\\\ \n",
      "  & (0.019) \\\\ \n",
      "  tmin10 & $-$0.618$^{***}$ \\\\ \n",
      "  & (0.035) \\\\ \n",
      "  Constant & 0.000 \\\\ \n",
      "  & (0.037) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes \\\\ \n",
      "Date FE & Yes \\\\ \n",
      "State:Month FE & Yes \\\\ \n",
      "Observations & 4,405,171 \\\\ \n",
      "R$^{2}$ & 0.0001 \\\\ \n",
      "Adjusted R$^{2}$ & 0.0001 \\\\ \n",
      "Residual Std. Error & 77.175 \\\\ \n",
      "F Statistic & 312.043$^{***}$ \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{1}{r}{Standard errors are in parentheses and are clustered on the 1st admin level} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# invisible(stargazer(history,\n",
    "#           type='latex',\n",
    "#           title = \"Spline Regression Model for Climate Sleep Loss Projections\",\n",
    "#           #out='linear_tmin.tex',\n",
    "#           model.numbers = TRUE,\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the 1st admin level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "short_7hr_sleep ~ tminneg20 + tmin10"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # SHORT Sleep (<7hr) spline probability forecast plot (extract coefficient values)\n",
    "\n",
    "# #drop extra columns and remove rows with NA values for demeanlist \n",
    "# sleep <- as.data.table(Climate_Controls_DF)\n",
    "# #sum(is.na(sleep$tmin))\n",
    "# subcols <- c(\"short_7hr_sleep\", \"tmin\", \"userID\", \"stateyrmn\", \"unique_day_no\", \"CUTPRCP\",\n",
    "#              \"CUTCLOUD_rean2\",\"CUTRHUM_rean2\", \"CUTWIND_rean2\",\n",
    "#              \"CUTTRANGE\",\"CUTTMIN.NORM1981_2010\", \"CUTPRCP.NORM1981_2010\", \"CUTTRANGE.NORM1981_2010\", \n",
    "#              \"CUTCLOUD.NORM1981_2010_rean2\", \"CUTRHUM.NORM1981_2010_rean2\", \"CUTWIND.NORM1981_2010_rean2\")\n",
    "# sleep <- na.omit(sleep, cols = subcols)\n",
    "# sleep <- subset(sleep, select = subcols)\n",
    "\n",
    "# #set knots\n",
    "# k <- c(-20,10)\n",
    "\n",
    "# ## knot for linear splines\n",
    "# for (i in k) {\n",
    "#   sleep <- sleep[, paste0(\"tmin\",i) := tmin - (i)]\n",
    "#   sleep <- sleep[tmin < i, paste0(\"tmin\",i) := 0]\n",
    "# }\n",
    "# setnames(sleep, names(sleep), gsub(\"-\", \"neg\", names(sleep)))\n",
    "# sleep <- subset(sleep, select = -c(tmin))\n",
    "\n",
    "# ## use demeanlist from lfe package to take out fixed effects to be able to use predict.lm\n",
    "# demean <- demeanlist(mtx = sleep[, .SD, .SDcols=(names(sleep) %like% \"short_7hr_sleep|tmin\") \n",
    "#                                  & !(names(sleep) %like% \"cuttmin_norm_w7\")],\n",
    "#                      fl = list(userID = sleep[, userID],\n",
    "#                                stateyrmn = sleep[, stateyrmn],\n",
    "#                                unique_day_no = sleep[, unique_day_no],\n",
    "#                                cutprcp = sleep[, CUTPRCP],\n",
    "#                                cutcloud_rean2 = sleep[, CUTCLOUD_rean2],\n",
    "#                                cutrhum_rean2 = sleep[, CUTRHUM_rean2],\n",
    "#                                cutwind_rean2 = sleep[, CUTWIND_rean2],\n",
    "#                                cuttrange = sleep[, CUTTRANGE],\n",
    "#                                cuttmin_norm_w7 = sleep[, CUTTMIN.NORM1981_2010],\n",
    "#                                cutprcp_norm_w7 = sleep[, CUTPRCP.NORM1981_2010],\n",
    "#                                cuttrange_norm_w7 = sleep[, CUTTRANGE.NORM1981_2010],\n",
    "#                                cutcloud_norm_7_rean2 = sleep[, CUTCLOUD.NORM1981_2010_rean2],\n",
    "#                                cutrhum_norm_7_rean2 = sleep[, CUTRHUM.NORM1981_2010_rean2],\n",
    "#                                cutwind_norm_7_rean2 = sleep[, CUTWIND.NORM1981_2010_rean2]), \n",
    "#                      progress = 5)\n",
    "                     \n",
    "# ## conduct the regression on the demeaned data\n",
    "# cov <- grep(\"tmin\", names(demean), value=TRUE)\n",
    "# cov <- paste0(cov, collapse=\" + \")\n",
    "# fmla <- as.formula(paste(\"short_7hr_sleep\", \"~\", cov))\n",
    "# fmla\n",
    "# history <- lm(fmla, data=demean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = fmla, data = demean)\n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-1.3222 -0.3743  0.0425  0.3573  1.2960 \n",
       "\n",
       "Coefficients:\n",
       "              Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept) -1.423e-17  2.082e-04    0.00 1.000000    \n",
       "tminneg20    3.951e-04  1.083e-04    3.65 0.000262 ***\n",
       "tmin10       2.978e-03  1.998e-04   14.91  < 2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 0.437 on 4405168 degrees of freedom\n",
       "Multiple R-squared:  9.377e-05,\tAdjusted R-squared:  9.332e-05 \n",
       "F-statistic: 206.6 on 2 and 4405168 DF,  p-value: < 2.2e-16\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summary(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{Table 37B: Spline Regression Model for Climate Short (<7hr.) Sleep Projections} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{1}{c}{Dependent Variable: Short <7hr. Sleep Probability} \\\\ \n",
      "\\cline{2-2} \n",
      "\\hline \\\\[-1.8ex] \n",
      " tminneg20 & 0.0004$^{***}$ \\\\ \n",
      "  & (0.0001) \\\\ \n",
      "  tmin10 & 0.003$^{***}$ \\\\ \n",
      "  & (0.0002) \\\\ \n",
      "  Constant & $-$0.000 \\\\ \n",
      "  & (0.0002) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes \\\\ \n",
      "Date FE & Yes \\\\ \n",
      "State:Month FE & Yes \\\\ \n",
      "Observations & 4,405,171 \\\\ \n",
      "R$^{2}$ & 0.0001 \\\\ \n",
      "Adjusted R$^{2}$ & 0.0001 \\\\ \n",
      "Residual Std. Error & 0.437 \\\\ \n",
      "F Statistic & 206.556$^{***}$ \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{1}{r}{Standard errors are in parentheses and are clustered on the 1st admin level} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# invisible(stargazer(history,\n",
    "#           type='latex',\n",
    "#           title = \"Spline Regression Model for Climate Short (<7hr.) Sleep Projections\",\n",
    "#           #out='linear_tmin.tex',\n",
    "#           model.numbers = TRUE,\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Short <7hr. Sleep Probability',\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the 1st admin level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#SI Figs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Supplementary Figure 1: Weather attributed changes in person-level sleep duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 760127 rows containing non-finite values (stat_bin).”Warning message:\n",
      "“Removed 2 rows containing missing values (geom_bar).”"
     ]
    },
    {
     "data": {
      "image/png": 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6ZNi4qKotMj0U6ePFmRXSsQQkx+fn5Hjhx5+eWXMzMz6eQUllq0aFFzZtchhCpu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KycFLmSm1T+pbmXKK8nEjwHAgBACAo74F/7bmKvIoQk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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#binned flex_t relative humidity plot\n",
    "#load saved model output\n",
    "dt <- read.csv(file=\"model_data/Fig2A_binned_tmin_sleep_duration_model_output.csv\",header=T)\n",
    "flex.plot <- binned.plot(felm.est = flex_t,\n",
    "                 plotvar = \"CUTRHUM_rean2\",\n",
    "                 breaks = 20,\n",
    "                 omit = c(60,80),\n",
    "                 ylimit = c(-7.5,.5),\n",
    "                 linecolor = \"#0CBAA6\",\n",
    "                 pointfill = \"#0CBAA6\",\n",
    "                 errorfill = \"#0CBAA6\",\n",
    "                 xlabel = \"% relative humidity\",\n",
    "                 ylabel = \"Change in Sleep Duration (Minutes)\"\n",
    "                 )\n",
    "# hist <- axis_canvas(flex.plot, axis = \"x\") + \n",
    "#         geom_histogram(data = Climate_Controls_DF, aes(x = Climate_Controls_DF$rhum_rean2), bins=50, fill='gray60', color='gray60', alpha=0.75, size=0.1)\n",
    "# flex.plot <- insert_xaxis_grob(flex.plot, hist, position = \"bottom\", height = grid::unit(0.1, \"null\"))\n",
    "flex.plot <- ggdraw(flex.plot)\n",
    "#ggsave(\"flexplot_rhum.pdf\")\n",
    "flex.plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 1781500 rows containing non-finite values (stat_bin).”Warning message:\n",
      "“Removed 2 rows containing missing values (geom_bar).”"
     ]
    },
    {
     "data": {
      "image/png": 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N9UNM+PI50dr2D3xBNPOI6QqD2WAgAAAEBgkpYU0IaIWOMxiTVT6GrqF69g17Vr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4Zs0abs85c+Zwez733HPcnj/99BO3Z8+e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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#binned flex_t precip plot\n",
    "#load saved model output\n",
    "dt <- read.csv(file=\"model_data/Fig2A_binned_tmin_sleep_duration_model_output.csv\",header=T)\n",
    "flex.plot <- binned.plot(felm.est = flex_t,\n",
    "                 plotvar = \"CUTPRCP\",\n",
    "                 breaks = 1,\n",
    "                 omit = c(0,0),\n",
    "                 ylimit = c(-2.5,5),\n",
    "                 linecolor = \"#87ceff\",\n",
    "                 pointfill = \"#87ceff\",\n",
    "                 errorfill = \"#87ceff\",\n",
    "                 xlabel = \"Precipitation in cm\",\n",
    "                 ylabel = \"Change in Sleep Duration (Minutes)\"\n",
    "                 )\n",
    "# hist <- axis_canvas(flex.plot, axis = \"x\") + \n",
    "#         geom_histogram(data = Climate_Controls_DF, aes(x = Climate_Controls_DF$prcp), bins=20, fill='gray60', color='gray60', alpha=0.75, size=0.1)\n",
    "# flex.plot <- insert_xaxis_grob(flex.plot, hist, position = \"bottom\", height = grid::unit(0.1, \"null\"))\n",
    "flex.plot <- ggdraw(flex.plot)\n",
    "#ggsave(\"flexplot_precip.pdf\")\n",
    "flex.plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 1414827 rows containing non-finite values (stat_bin).”Warning message:\n",
      "“Removed 2 rows containing missing values (geom_bar).”"
     ]
    },
    {
     "data": {
      "image/png": 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Yi8/vrrb7zxhrNLlojIY489tmLFCkc2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\nAAA04v8A+PUxkq92P3EAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#binned flex_t wind plot\n",
    "#load saved model output\n",
    "dt <- read.csv(file=\"model_data/Fig2A_binned_tmin_sleep_duration_model_output.csv\",header=T)\n",
    "flex.plot <- binned.plot(felm.est = flex_t,\n",
    "                 plotvar = \"CUTWIND\",\n",
    "                 breaks = 5,\n",
    "                 omit = c(0,5),\n",
    "                 ylimit = c(-2.5,5),\n",
    "                 linecolor = \"blue4\",\n",
    "                 pointfill = \"blue4\",\n",
    "                 errorfill = \"blue4\",\n",
    "                 xlabel = \"Windspeed in\",\n",
    "                 ylabel = \"Change in Sleep Duration (Minutes)\"\n",
    "                 )\n",
    "# hist <- axis_canvas(flex.plot, axis = \"x\") + \n",
    "#         geom_histogram(data = Climate_Controls_DF, aes(x = Climate_Controls_DF$wind_rean2), bins=15, fill='gray60', color='gray60', alpha=0.75, size=0.1)\n",
    "# flex.plot <- insert_xaxis_grob(flex.plot, hist, position = \"bottom\", height = grid::unit(0.1, \"null\"))\n",
    "flex.plot <- ggdraw(flex.plot)\n",
    "#ggsave(\"flexplot_wind.pdf\")\n",
    "flex.plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 428682 rows containing non-finite values (stat_bin).”Warning message:\n",
      "“Removed 2 rows containing missing values (geom_bar).”"
     ]
    },
    {
     "data": {
      "image/png": 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xcqrAEAAACiPNiJyJtvvvnWW2/ZO2WJ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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#binned flex_t cloud plot\n",
    "#load saved model output\n",
    "dt <- read.csv(file=\"model_data/Fig2A_binned_tmin_sleep_duration_model_output.csv\",header=T)\n",
    "flex.plot <- binned.plot(felm.est = flex_t,\n",
    "                 plotvar = \"CUTCLOUD\",\n",
    "                 breaks = 20,\n",
    "                 omit = c(0,0),\n",
    "                 ylimit = c(-2.5,5),\n",
    "                 linecolor = \"grey1\",\n",
    "                 pointfill = \"grey1\",\n",
    "                 errorfill = \"grey1\",\n",
    "                 xlabel = \"% Cloud Cover\",\n",
    "                 ylabel = \"Change in Sleep Duration (Minutes)\"\n",
    "                 )\n",
    "# hist <- axis_canvas(flex.plot, axis = \"x\") + \n",
    "#         geom_histogram(data = Climate_Controls_DF, aes(x = Climate_Controls_DF$cloud_rean2), bins=21, fill='gray60', color='gray60', alpha=0.75, size=0.1)\n",
    "# flex.plot <- insert_xaxis_grob(flex.plot, hist, position = \"bottom\", height = grid::unit(0.1, \"null\"))\n",
    "flex.plot <- ggdraw(flex.plot)\n",
    "#ggsave(\"flexplot_cloud.pdf\")\n",
    "flex.plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CKqq6v773//u3z58tOnsa9U68XLuhHpiYgqP0JhBwAAIC73FnZFRUW//s/x48fN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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#binned flex_t diurnal temperature range plot \n",
    "#load saved model output\n",
    "dt <- read.csv(file=\"model_data/Fig2A_binned_tmin_sleep_duration_model_output.csv\",header=T)\n",
    "flex.plot <- binned.plot(felm.est = flex_t,\n",
    "                 plotvar = \"CUTTRANGE\",\n",
    "                 breaks = 5,\n",
    "                 omit = c(5,10),\n",
    "                 ylimit = c(-5,5),\n",
    "                 linecolor = \"orange2\",\n",
    "                 pointfill = \"orange2\",\n",
    "                 errorfill = \"orange2\",\n",
    "                 xlabel = \"Diurnal Temperature Range in C\",\n",
    "                 ylabel = \"Change in Sleep Duration (Minutes)\"\n",
    "                 )\n",
    "# hist <- axis_canvas(flex.plot, axis = \"x\") + \n",
    "#         geom_histogram(data = Climate_Controls_DF, aes(x = Climate_Controls_DF$trange), bins=41, fill='gray60', color='gray60', alpha=0.75, size=0.1)\n",
    "# flex.plot <- insert_xaxis_grob(flex.plot, hist, position = \"bottom\", height = grid::unit(0.1, \"null\"))\n",
    "flex.plot <- ggdraw(flex.plot)\n",
    "#ggsave(\"flexplot_trange.pdf\")\n",
    "flex.plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Supplementary Figure 2: Heat index sensitivity analyses\n",
    "#Binned Nighttime Heat Index FE Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ cutheat.index + tmin_norm_w7 +      CUTPRCP + prcp_norm_w7 + CUTTRANGE + trange_norm_w7 + CUTCLOUD_rean2 +      cloud_norm_7_rean2 + rhum_norm_7_rean2 + CUTWIND_rean2 +      wind_norm_7_rean2 | userID + unique_day_no + stateyrmn |      0 | state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-384.73  -49.12   -2.32   45.76  408.79 \n",
       "\n",
       "Coefficients:\n",
       "                        Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "cutheat.index(-Inf,-15]  2.48468      0.76430   3.251 0.001150 ** \n",
       "cutheat.index(-15,-10]   1.14027      0.65421   1.743 0.081338 .  \n",
       "cutheat.index(-10,-5]    0.61594      0.56945   1.082 0.279416    \n",
       "cutheat.index(-5,0]      0.20629      0.43568   0.473 0.635866    \n",
       "cutheat.index(0,5]       0.59804      0.24044   2.487 0.012873 *  \n",
       "cutheat.index(10,15]    -2.19721      0.20511 -10.712  < 2e-16 ***\n",
       "cutheat.index(15,20]    -4.43458      0.36035 -12.306  < 2e-16 ***\n",
       "cutheat.index(20,25]    -6.51280      0.48088 -13.544  < 2e-16 ***\n",
       "cutheat.index(25, Inf]  -7.06901      0.68781 -10.278  < 2e-16 ***\n",
       "tmin_norm_w7            -0.21947      0.07729  -2.840 0.004518 ** \n",
       "CUTPRCP(0,1]             0.27432      0.10525   2.606 0.009152 ** \n",
       "CUTPRCP(1,2]             1.21583      0.28298   4.296 1.74e-05 ***\n",
       "CUTPRCP(2,3]             1.29580      0.34993   3.703 0.000213 ***\n",
       "CUTPRCP(3, Inf]          2.09932      0.53497   3.924 8.70e-05 ***\n",
       "prcp_norm_w7             2.17204      1.09752   1.979 0.047812 *  \n",
       "CUTTRANGE(-Inf,0]        2.70243      1.20440   2.244 0.024845 *  \n",
       "CUTTRANGE(0,5]           0.22327      0.17626   1.267 0.205274    \n",
       "CUTTRANGE(10,15]        -0.54812      0.12498  -4.386 1.16e-05 ***\n",
       "CUTTRANGE(15,20]        -2.03702      0.27719  -7.349 2.00e-13 ***\n",
       "CUTTRANGE(20, Inf]      -2.29470      0.71893  -3.192 0.001414 ** \n",
       "trange_norm_w7          -0.02994      0.14615  -0.205 0.837698    \n",
       "CUTCLOUD_rean2(0,20]     0.49807      0.43762   1.138 0.255066    \n",
       "CUTCLOUD_rean2(20,40]    0.34388      0.46785   0.735 0.462326    \n",
       "CUTCLOUD_rean2(40,60]    0.59971      0.45639   1.314 0.188839    \n",
       "CUTCLOUD_rean2(60,80]    0.93298      0.48518   1.923 0.054484 .  \n",
       "CUTCLOUD_rean2(80,100]   1.53951      0.45928   3.352 0.000802 ***\n",
       "cloud_norm_7_rean2      -0.02813      0.02288  -1.229 0.218885    \n",
       "rhum_norm_7_rean2        0.10793      0.04130   2.613 0.008970 ** \n",
       "CUTWIND_rean2(5,10]      0.48058      0.11747   4.091 4.29e-05 ***\n",
       "CUTWIND_rean2(10,Inf]    0.78516      0.22938   3.423 0.000619 ***\n",
       "wind_norm_7_rean2       -0.33645      0.19455  -1.729 0.083743 .  \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.71 on 4345333 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2842   Adjusted R-squared: 0.2744 \n",
       "Multiple R-squared(proj model): 0.0001844   Adjusted R-squared: -0.01358 \n",
       "F-statistic(full model, *iid*):28.84 on 59837 and 4345333 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 20.42 on 31 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #Alternative Thermal Measure: Heat Index and Sleep Duration FE Regression\n",
    "# flex_hi <- felm(formula = sleep_duration_minutes ~ cutheat.index + tmin_norm_w7 + \n",
    "#                                           CUTPRCP + prcp_norm_w7 + \n",
    "#                                           CUTTRANGE + trange_norm_w7 + \n",
    "#                                           CUTCLOUD_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_norm_7_rean2 + \n",
    "#                                           CUTWIND_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(flex_hi)\n",
    "# #save model coefficients for plotting\n",
    "# dt <- as.data.frame(summary(flex_hi)$coefficients[, 1:4]) # extract from felm summary (use df to keep coefficient\n",
    "# write.csv(dt,file=\"model_data/SIFig2B_heatindex_sleep_model_output.csv\")#save data as CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 2439306 rows containing non-finite values (stat_bin).”Warning message:\n",
      "“Removed 2 rows containing missing values (geom_bar).”"
     ]
    },
    {
     "data": {
      "image/png": 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bafYEdEvEU6uMxatrfOk7Kskvreq0oYjrOKXlGwR2PC+xNJFPI36vizv8sBAIDA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2QBoP55pkBxE4ACxQDg6649SWXflxa+tP2FefpCkesB\n0eyARSpWwRAREjuAhuH5xRMAANAoRf6H2iYF3/F+/GwVI/YwqcjmUzcYqz7/74WshUyZDRch\ngO9xw69Q1mrD/v7+0nsDAIDGQaYhmYaIQrtw7cfIL/9kqt4ka6c5PI6L7CMjorykyqjoKaQ7\nRG0OkbxtAwcL4CPckNjNmoUyRQAAvkuTKBN4Kk7li85Zql89u9o4cDGrjmD9FUuo/AciomtP\nUptDDR0lgG/w/KvY9957b8GCBZ6OAgAAXMewFP+cQh4oUtnOXCkkLzcKFqo0vGI0D+P5FqRZ\n1fARAvgIt81mLS4u3rNnT3Z2tuPrCYqLiw8fPnz06NHJkye7KwwAAGh4mkRZ7hFz3AzlySV6\nqrYkryTVcvknY8dxiuLK71imwnKqpSbRE1EC+AA3JHZFRUWvv/76F198YbGIDMIDAIAvqFr3\n2vpeLmunyLPgymZTaFcuvHuwRQgmFEABqDdSX8UWFRUNHTp07dq1yOoAAHyaYPJXfjBgxojA\ntiIvZAWezq4ymspvjeahcDFAfZCa2M2ZMyc5OdktoQAAQCOW90Kgep5KfSjxlR9kKpHczlDE\nJ6802L6oRW4H4HaSErtLly599dVX7goFAAAasfDXiPXnhTB/DRszSSHapOC0JXN79aooAhnO\n1Xd0AD5CUmK3ceNG6REEBQUNGDBAej8AAOBJ8jbUYiPb+YLOODH6LlnzQeJT6C5+ayxL562H\nuUf0lPs3yuhLOhRAAXADSYldSkqK7WG7du2+/PLLbdu2LViwgGVv9fzOO+/s37//66+/fued\nd+Li4qznFQrFjh07bty4MW3aNClhAACAVwi4n7jIqlURsc8q/JqLvJDlzXTmY71Zd/ONrFrx\nf1TyOQk6uvYUCSIljgHAKZISu+zsbOtnhmF+++23p59+esSIEe+8887cuXOtl9LS0u68886n\nnnpqwYIFZ86c+eGHHxQKBREZjcbp06eXlpZKiQEAALwQp2QSZqsYsWE7bZ5wYd3NHE5nnKg1\nzOSFKGr5IzHyBg0RoCmSlNjl5ORYP3fp0qVr167Ww3vvvdf6+ZdffjGbb86QZRjm0UcfXbZs\nWdVhRkaGbQoIAABNQNWgXVB7ttN48cl21w6Yrh24+Vwo031YWHY493RCw8UH0HS5LbHTaDS2\nl3r37m39XFJScu7cbRNjn332WT8/v6rP33zzzYkTJ6SEAQAA3qYqt2v3gLxZT060wfnPjZXX\nq17IyixCK8IiWQB3kJTYcdytf65lZWW2l8LDw9u2bWs9PHLkiN2NCQm3fjn75ptvpIQBAABe\niqHuM5XKUJHJdhaDkPyJnr89l0NuByCRpMQuKCjI+vnKlSsGg8H2qu2g3a5du+zu5Xm+lqsA\nANDYafoUBPtPCQ7/Kn62khF72pSl8xf/z2h3ErkdgBSSErtOnTpZPxcXF3/44Ye2V20Tu+3b\nt+fm5loPCwoKzp49az3MysqSEgYAAHgdvpLS49XybwLVr0XEFrd7UHxhROY2U/4J+0zuZm6n\nOyJyAwDUSlJi17dvX9vDefPmjRw58pNPPql6LTt48GDrpYqKinHjxp05c0ar1aakpIwfP16r\n1VqvyuVYCQUA0LSw/hQynYh4IYplbnQcpwjtLPbEESjlU6O+mL/tJGPRJs+kzIFU+mWDxArQ\ndDCCINTdqgZJSUmJiYnVz58+fTohIYHn+ebNm+fn59fZz8CBAw8damSlKbds2TJp0qSSkhJP\nBwIA4K0EHZV8RiEzcpMYItIX8n++rjdViDx0QrtyfReorK9rFbIDYQHDiHhig6hDOnFhDRk1\nQKMmacSuf//+d9xxR41ds+zo0aMd6adbt25SwgAAAG/EqCn0eWt1OlU4G/usePWT4guW9C23\nqhMbzYPLtB/zQihFb0VWB+AUSYkdEX3++ee2SyjszJw503YLippMmTJFYhgAAOC1qkqfEFFU\nf1n0PeJbjV360ViSeuuFrNY4o6DsbG4yNpwEcI7UxC4mJmbPnj3NmzcXvdqrV6833nij9h6m\nTp06cOBAiWEAAIA3s+Z2XScpA1uLPHoEnk4vM5jKb72o5YUowiJZACdJTeyIqHfv3hcvXnz7\n7be7dOlS/eq777777rvvymQiv6IxDDN9+vTVq1dLjwEAABoFVk7xs5WcUqSynaGIT1lrX/2E\nkNsBOEPS4onqcnJy0tLSevbsGRwcbHs+Ozt748aNqampV65cyc/PDw8P79OnzxNPPNGjRw83\nfntDwuIJAABnWVO0q7vM5z83iLbp9oyy1b0iYwHWMT8AqIWbEzvfgcQOAMBZhccOBajeKdF+\nKwhByZ8Yrv8pMhTHyinxHXVgG5EXSppEGVX+Qf7DiEQG/ACAiCT9AvTZZ59ZLBbbM927d69l\nnSwAAPiu8h/CAycQ8YGqhWW6pd2mKkouWXT59oMLvInOLNUPWKyu9rpWqDi9MED1DoUvoGbv\nNFjUAI2LpMTuhRdesK0zTEQLFy5EYgcAACL8R5CsOZlzWPY6ES/zYxP+rjr6lo632DeszBX+\n+tIYO01pe5JjL/urPiQiKnqfgieSonNDxQ3QmEhaPFF9tURgYKCUDgEAoMliA0mzhqK3lFR+\nX/X0Ce7AdnhUvLJd9l7ztUO3vai18B1LKv6PF0KKK35CVgdQE0mJ3YgRI+zOZGZmSukQAACa\nsoD7KWC07TKIdqPlEXGcaNvza42Vube9qDWYRxaUpRlM9o8eALCSlNi9+uqr4eHhtmfOnj0r\nLR4AAPAhDEvdZymVISKLISwG4ewneuH29RW8EEIogAJQM0mJXXBw8ObNm21fv+7fv3/Xrl2S\nowIAgKbMdtBOGcx0n6EUXedaeoVP+0Gksh0htwOogdQCxYMGDdqzZ098fLz1zJQpU7Zt2yax\nWwAAaNpsc7uIBK7tA3LRZum/mm6crLa8goiQ2wGIkVrv8T//+Q8RPfnkkzzPp6SkEFF2dvb9\n99/fr1+/7t27t2vXTq1W19nJSy+9JDEMAABo1DpNUJSkWkrSePsLAqV8ahi4WK0MFRnTyz1i\n1iTKSLuf/AYRic/VA/ApUgsUM4wbqkQ2xiLJKFAMACCZUHryC5a5UWl4mYi0ecLhN3RmncgT\nISyW6zNPxYjtMeuvXBKoXkBhf6fIj+o9XgCv54a9YgEAAFyRMy7Y79kA9Vsy9gIR+UUxsdPE\nq58UnbNk/Gaqfp5lr/urlhDxVPQx6U/Xb7QAjQESOwAA8JCAUVX/Xy47VvVBkyhrOUR8jlDa\nd8bqL2p5vkVJxSZeCKWWP5KqsW4+DuBGSOwAAMBDgidR2Bym3Smd8Wnrua6TFf4tRSb5CDyd\n+dhgrrR/UWu0DCoou5h7bnT9hgrQSCCxAwAAT2Eo8kNSdrM9xamYHi+qWIVIbqcv5FPWilQ/\nQXE7ACupq2LXr1/vjjAAAMB3aRJltmlZQDTb+QnFX+sN1VvmJZmz93LRQ8UfXjcXyQL4MKn/\nACZNmuSWOAAAwJfZ5XZthsuKUiz5x0UG4S6sMwR3YANbi79xQm4HPg6vYgEAwPswFDdDoY4Q\neUjxJkpebuCNNdbJupkgmnPrLzoAr4XEDgAAvILdSJvMn4mbpRCrXUcVV/m/NohvNXZTyRq6\n0oG0e90aIEAjgMQOAAC8haafIVD9BsdmVB2GduHaPyy+1djVneZfZjLEAAAgAElEQVTcw+Kr\nJRSyfZQ7nXgt5YwnS3E9hQrgnaRORHjjjTekB9G7d+9HH31Uej8AANCIma5Q1t3+ykwZd764\nYkvVuQ6PKEpS+cIUke1iz31mDO7IqpvZj1AYzXdpjTPUii+YyH8TF1rvYQN4E6/YUmzy5Mnr\n1q2T3k9DwpZiAADuZqGMRNIf54WowvIkC9+y6qyhiD/0ut5ULvK0Cu7A9vunmhXZJNYs586Z\nLAlYSAG+Bq9iAQDAS3CkWUUhMwrKzlqzOiJShrFxM5UkNoxQepm/vEl0sp3MZEkgFLcD34Nf\nZQAAwGuo+pCmT6TGPiFr1pNrM1yWuePWyXTDyT3lX1w1ptBKoUdS/NQp0/p0TxTtEgVQwKdg\nxA4AALxO9VSs81PKwLY3n1n7ytcvyh0pEH9P0LPDAqcXn2UfnjZs7fefNHiYAF4Hv8QAAEAj\nwMoo4e+qw2/ociouflP06uxmX/XwG1F1aVDAE/38Hn5nxWMDew2J7RRf/V6bQTseIxrQtElN\n7LZu3epgS61We+7cuW3bth09erTqTEBAwLJlyyIjI6OjoyWGAQAATYzdXhRE5N+c6TpFsfFf\n33VT3WXN6qrEqu/qobj/+9/Wv/3ih6K95R4xa7r9QEVLqPUeYoPrMW4Aj5Ka2D3wwAOONx43\nbtzChQuXLl360ksvEVFFRcXixYv379+v0WgkhgEAAL6g5RBZ8cpL7ct6Vb/UXtH7woX9Nd2o\nVnxB16YTEV17mqJ/xrgdNFUN/ZPNsuzcuXPHjx9fdXjx4sWpU6c2cAwAANAoaBJlHJvhr/zI\n9mREJ5VR0FVvbBS0hmxFTVuNGUxjLHxHEjhSD0RWB02YZ36458yZY/38+++/79y50yNhAACA\nVytd3ywkIVD9qlL2h/Vc7/jEZN1OgXjbhgIJp3U72gh9atpqjBfCiis3F1f+mpv2Uv3GDOBR\nnknsYmNjbSsbb9iwwSNhAACAV5O3J15HRGrFrSL24x942hxU+k3hq2bhZg5nEUzfF71ZYM66\nK3Dy1Z3m/OPitevMlhiD+d4GiBrAgzyzKtZuvwrrcgoAAIBb/O6k0Nmk6FRycZr1XKB/0LdL\nf3vmtbEvZcd1VPZjGTZNn8Qx8rmRPwSwYUSUssY4sAOrCq1x5AKV7aAJ88xP9unTp223Mrt2\n7ZpHwgAAAG8XtYyINP1vK1nctUP3//7f2e27t+389IRJzyf6PxqvvlfOqKqumsqF5E+MfReo\nmJpfSiG3g6bKAz/WOp3Odo4dEZlMpoYPAwAAGi+5XPHgiIcGaEad+Leeqq2XKL5gyfjN1O5B\neS09ILeDJknqz/T+/TWuLbcjCEJ+fv5ff/21evXq69ev216KjIyUGAYAADRt1cvaEVFED671\nvbKsnSKT6i59bwzrygV3rG0qee4RsyZ2CxnOUcRCd8YK4DlSE7u77rpLehD9+vWT3gkAAPig\nmInKklS+LJO3O89bKHm5fsBitUzFiN5IRP7KjyjnNSIiRQcKerJe4wRoGF5Ry2fs2LGeDgEA\nALyd6JtTVkZxs5SsQiR70+YJqV+KVz+pYua7ksASEZly3BQjgId5PrHr2bPnuHHjPB0FAAA0\nAppEmUK2Vy47ZnsyoBUb86RCtH32PvP1P8WrnxCRwTSiTP9hSeX3FP6qmwMF8BAPJ3atW7f+\n+eefWdbz+SUAAHg7wUTXnw4LuC9YPY3otlV3re+VRfYWn1x0/nOj7ob9i1orreE5venh6rP3\nABopj2VUSqXy2WefPXXqVJs2bTwVAwAANCaMnHgtkSDjUpTy7bdfou7TFcpQkReyZq2QvMIo\n1Jja3YTcDpoGqYsnZsyY4VR7lUoVFhYWFxd35513hoWFSfx2AADwLVEfkymdmr1vOHuX3RV5\nIBM/W3l8kb56DleSarmy2dThkdqqnxAKoECTIPUneNWqVW6JAwAAoG6yltT2BBERiQywhXXj\n2twvz9gqUhv18iZjWHcutHMd76mQ20Fjh8ltAADQ+NSUfnWaoAjuIPJoE3hKXmYwV1arZSyq\ncgfpkqSEB+ApSOwAAKDpYDmKf14lWrtOX8ifW1tb9ZMqZSdXU/YoyhlDpqv1ECBA/XLngHN+\nfn5OTk5RUVFhYSHDMOHh4eHh4S1btoyIiHDjtwAAAFANe1EQkV8UE/O04twaQ/VLuUnmZgfY\nFoNrm2wn55JIMJM5lyp+odBZbgsXoEFITez0ev2PP/64a9eugwcPXr58WbRN586dBw0aNGzY\nsLFjxyoU4qWGAAAAnFVTbhc9VFaUYhGtYHf+c1NIR5lf8xq3oyjVrWTZa3rT+ODQZ9wZK0CD\nYATBsQkH1eTn53/00Udr164tLCx08JbIyMgZM2b8/e9/Dw8Pd+1LvceWLVsmTZpUUlLi6UAA\nAHxabpJBLfs/nflxEjjb82at8Odrel2BSJmToPZs4j/VTG0jGwIRQzXP5APwWi7Osfvxxx9j\nY2MXL17seFZHRPn5+W+//Xa3bt02b97s2vcCAADcYvxLE3VnsP8Uf8UndldkfkzcLAUj9pQr\nu8Jf2lT7ZLub43kobgeNjtOJnSAIM2fOHDduXEFBgWtfmZ+f//DDDz///POu3Q4AAHAToyLj\nOSLyV37AMDq7i6FduHYPiU+nS//FVJhiqffwABqc04ndiy++uHr1aulfvHz58jfeeEN6PwAA\n4LvkbSniH+R3V1HFPkFQV7/ecawipJN49ZOzq4ym8ronI2HQDhoX5xK7n3/+edmyZXU2Cw0N\ndWQW3eLFi3fu3OlUAAAAALcJm0ut90b06yp6keEofrZKphZZKmEo4lMcqH5C1txOf5R4rYRA\nARqCE4kdz/MLFy4U6YJlR4wYsWbNmgMHDly+fFmn0xUVFRUUFOj1+itXrhw8eHDt2rUjR47k\nOK76vfPnz3c9dgAAgLoeZOpIJnaaeEGG/GPm7N0ODciVnPyaMofQ9aeJXFxxCNAwnFgVu2/f\nvqFDh9qdHDhw4OrVq+Pi4uq8/dy5czNnzjxw4IDd+T///HPAgAEOxuA9sCoWAMDb1PLaNHmF\n4fpBkauckhmwSO3fssbqJ0TEUGVEUALHZhIRRf9CAQ9KjhSgvjgxYrd//367M8OHD9+9e7cj\nWR0RxcbG/vHHHyNGjLA7v2/fPsdjAAAAqEkt1Ulin1X4a0SyN4tBOL1Uz4vsLnuLQP7FFb/y\nQli57j1kdeDlnEjs7AbbgoKC1q9fr1KpHO9BqVSuX78+ODjY9uR///tfx3sAAABwAadk4p5X\nsSJzgqgim7/4fR2T7cx814KyvyoNr9RLcADu40Rid/XqbbvmjRs3rnnz5s5+X1RU1GOPPWZ7\nJisry9lOAAAARGkSZSxTrJRvrX4puD3bYaz4ZLvM3003TtZR/YQXQgmLZMHrOZHY2c0nu/vu\nu137ynvuucf2sKioyLV+AAAA7JVvjgyPD/EfL2MvVL/Y7iF5eHexUTuBUj41GEocmnSO3A68\nmeuJnQvDdaI3FhcXu9YPAACAPUsBmXMZMgaoF1W/yLAUN1MhDxSZbGcsE1JWGxxc84rcDryW\nE4md0XjbFAQ/Pz/XvtLf39/20GAwuNYPAACAvZCp5D+MQqaVasVr6SvD2NhnxV/IFpyxZO5w\nNGO7mdtZ8l2KEqC+uLhXLAAAgFdiqNV20nwqCAE1tYjqJ4seKr5+9uIGQ1km7+A3lZzYSJfb\nU9l3roQJUD+Q2AEAQBPDUa2lT4ioy9MK/xYiL2R5M51drrcY6n4jK2PTQvyeIL6ScqeS6bLL\nsQK4FxI7AABommrJ7TgVEz9bxYhdr8gWUr+te6sxM9+p3PAPIobCXiF5ewlhArgTEjsAAPBF\nQe3YzuPFJ9td3WnOP173ZLtK/WuF5QdzLy0gqm3jCoCGVNtIde02bNhw8OBBF260q4cHAABQ\nTzSJslpWsLZ9QF503nLjlEgFu7OrjXe8z6rCax/+YEyWfkSUe8Rc+5tfgAbjxF6xDFNfv5E4\nHoP3wF6xAACNRcHRs8SYzRaRDTCNZcKfr+oMpSKPodCuXN8FKsbhN1vI7cAb4FUsAAA0YTwV\nvBUe1CfEbzKRyNCdIojpPkMp+iq1+IIl47daN5G9HYrbgTdAYgcAAE0YS/pTDBllXLJaLl6X\nJKIH1/pe8cG2S98bSy85Wv3Ehgu3ALgHEjsAAGjSolaQvBVFLdWZnqipScxEZVAbkQcib6Hk\n5Xqz3tH5QrlHzFS5k9LjyJzjYrQA0iCxAwCAJk3eitpfodAXannksTKKm6VkFSJvZLV5QuqX\ndVc/qaKU76Cs+8lwnnIeJUHvYsAAEjgx07NPnz71FwcAAEB9YWRU1wrZgFZszJOKC+tEdrnM\n3mcOi+OaD6z7iWk0DzHxfeRcEqn6ESNeSwWgXjmR2B07dqz+4gAAAPCs1vfKCpMt+SdEkr/z\nnxtDOrHqZnW85hIEVXHFDwr5fn3JBE0U3omBB+DHDgAAfEUdFUkY6j5doQwVeSFr1grJK4yC\nA4sieKG53jiBsEgWPASJHQAA+JDaczt5IBM/Wylau64k1XJlsxPVTwA8AokdAAD4HKVsG8Po\nRC+FdePa3C8XvXR5k7H4ohOlTDBoBw0PiR0AAPgSc56m1fjQgNEBykU1Nek0QRHcQeT5KPCU\nvMxgrnRityTkdtDAkNgBAIAvYTjS/peI/JRLWSZXtAnLUfzzKplKZLKdvpA/t9bR6idVco+Y\nSXeQ8ue4ECyAs5DYAQCAL+EiKHIJKbsXVe7iBU1NrfyimJinxeuV5CaZrx1wYrKdSv69kHkP\nFS2lwvedjhbASUjsAADAxwQ/TW1PhvcZVHur6KGymmrXnf/cpL3u6AtZC9+RBI6IyJTpTJQA\nrkBiBwAAvoYhRnx5hJ1uUxXqCJEHpcUgnFmuFxybPmey9C7VfV6mW0qalU5FCeACJHYAAOCj\n6ihrRyTzY+JmKUSrn5Rd4S9tcnSynd44TmuYhYUU0ACQ2AEAgO+qM7cL7cK1e0h8eC/9F1Nh\nisWpr0NuB/UNiR0AAEBtOo5VhHQSr35ydpXRVO5E9RNCbgf1DIkdAAD4NE2ijMiklG+vqQHD\nUfxslUwtUv3EUMSnOFn9hJDbQX1CYgcAAL5Nf1QT1S/U/yGF7M+amqgjmdhp4tVP8o+Zs3c7\nnajlHjGT/iRV1phNArgGiR0AAPg2cy4ZUoj4QNXrtbTSJMqaDxKfkPfX18bKHOdeyKrkP1HW\nnZTzGBnOOnUjQO2Q2AEAgG8LGE2BYylgVEnlhtobxj6r8NeIvJC1GITTS/W8E0WLSSZLJr6S\n+HIq/dKpYAFqV8dqIGeZTKZ9+/YdO3bs5MmT165dKykpKS8vDwwMDA0NbdmyZb9+/QYMGDBg\nwACWRUIJAABeo8UGYpTNouuY/cYpmbjnVUcX6vhqa2ErsvmL3xu7PCX+ura6Ct1bMuayRWjl\nH4ntKMCd3JbY5ebmLl68eMOGDQUFBTW1+eGHH4iobdu2U6dOfeGFFwIDA9317QAAAK5jlA42\nDG7PdhirSNsosmAi83dTeDeuWS/Owa8s0X5FxJQfsdRZcgXAce4ZOVu1alVMTMzHH39cS1Zn\nlZGR8eabb3br1u3XX391y7cDAAC4hSM5VruH5OHdxbI3gVI+NRhKHJ9sd/OtLhbJghu5IbF7\n9dVXn3vuubKyMqfuys7Ofuihhz799FPpAQAAADQYhqW4mQp5oMhkO2OZkLLaQM6toyBCbgfu\nIzWxW7NmzQcffODavYIgzJgxY+vWrRJjAAAAcBdHBu2UYWzss+LT6QrOWDJ3IEsDj5GU2OXl\n5b388su1twkICGAYkV9rrKZPn+7saB8AAED90STKWCZPzp2spU1UP1n0UPEU8OIGQ1km7+yX\n3hy0M14gS92TmgBqIimx27BhQ3l5ud1JlUo1adKk7du3p6amVlRUlJeXa7Xaixcv7tixY/Lk\nyUql/QTVa9eubdy4UUoYAAAA7lS6LiIoPsT/MYYqa2nV5WmFfwuRkQveTGeX6y0Gp9/IFh/f\nRpkDKfthEgzO3gtQRVJi99NPP9mdeeaZZ3JyctavX3/fffd17tzZ39+fiFQqVadOnYYPH75u\n3bqrV68+8cQTdnchsQMAAC+iO8QyRRyb6adaXksrTsXEz1YxYsN2FdlC6rfObjXGB6jfJEsJ\n6Q5SyVon7wW4SVJil56ebnv42GOPff7552FhYbXc0qxZsw0bNjz88MO2J1NTU6WEAQAA4E7N\n/k2yaAp/TWt4sfaGQe3YzuPFJ9td3WnOP+7UZDu2uPInC99Sa5hNoc85cyPALZISO7viJosW\nLXLwxvffv60eY25urpQwAAAA3IkLow5p1GyxINRd367tA/JmPcVr151dbdQXOjHZjudbFJaf\nKNN9lHvE6Sl6AFUkJXaRkZHWzyEhIR07dnTwxk6dOoWHh1sPQ0NDpYQBAADgZoyKHFshSwx1\nn6FUBotMtjNXCskrjIIzSRovhNfdCKBmkhK7Ll26WD/r9XpBcHSiqCAIOp3OetimTRspYQAA\nAHiQIojpPkNJYhUgii9YMn5zZhPZ/0FlO3CNpMRuypQp1s96vX7Xrl0O3rh7926tVms9HDNm\njJQwAAAA6omD+31F9OBa3yve8tL3xtJLrrxaRW4HLpCU2I0bN27AgAHWw1mzZjmypVhhYeGs\nWbOsh1FRUVOnTpUSBgAAQP1xMLeLmagMaiPyVOUtlLxcb9Y7vx+FNbczZRJZXLgdfJCkxE4u\nl//000/WF7JpaWl9+/b9+uuvDQbxAjxGo3HDhg19+/a9ePFi1ZmAgIBvv/3Wdq4eAACAF5Jz\nJ1mmqJYGrIziZilZhcgbWW2ekPqls9VPbio8dogy+lLuTNduB1/DOD4xrrqLFy9ev369srJy\n3rx5Z86csZ5v1qzZww8/3K5du+jo6MjIyBs3bmRnZ6enp//888/5+fnWZoGBgR988IHtRD1R\nQ4YMcTnC+rNly5ZJkyaVlJR4OhAAAKhnvJYK3qSiZTrjxFLtmtrbZu00X1gnProR/7yy+UCH\nBv+sGEbfLDCGZa8REbX8ngIfc+p28EGSEru//e1vn332mRujESUlwvqDxA4AwFfwlZTenUwZ\nRExh+TGTJaG2xgKd+o8h/4TI9DiZHzNwsUrdzLl3ZQrZoVD/EXrjBHX8GmLkTt0LPkjSq1gA\nAICmj/UnzSqSaUq1n9eR1RERQ92nK5ShYtVPtE5XPyEio/mOwvKjpbq1yOrAEUjsAAAA6uI/\ngtpfDu41pe6WRPJAJn62khF7wJakWq5sdrr6iZnvSlgkC45BYgcAAOAA1o8cXiEb1o1re7/4\nANvlTcbiiy5uLIHcDuqExA4AAMD9Ok5QBHcUecgKPCUvM5grXZw+jtwOaufc8hw7Q4YMkckk\n9QAAANC4aBJljmRXLEfxs1WHX9dVr2CnL+TPrTUmvFj3RrSico+YNYkyMl8nWSSR+Da14LMk\nrYr1ZVgVCwDgsxwfNsveaz63Rrz6SdxzihaDXVwPIeNSIkIfpIAHSLPatR6gqcKrWAAAAOdU\nzbRTyPbX2TJ6qKym2nXnPzdpr7sytsKQMTRgNJmzqeRTKvvOhR6gCUNiBwAA4CRTuqblqLCA\nYUr5r3W27TZVoY4QedpaDMKZ5XrB+SlzAilKK78UBKXe+CgFYrN1uI37E7ucnJwff/zx9ddf\nHz169ODBg+Pj4zt37my9qtPpVq5cWVRU264sAAAAXs18jSr/IKIg9VyiOlIzmR8TP1shWv2k\n7Ap/aZMrW40ZzYOLKg6VaL8lRuXC7dCEuXOO3aZNm9asWbNr1y6et1/Ibf2W0tLSkJAQPz+/\nefPmvfrqq3J5Yy23iDl2AAA+LXcm6ZMKb6wyWXo70vzSD8bLP4lUsGNY6v2GKry762sgHKzA\nAj7CPSN2aWlpQ4cOffTRR3fu3Fk9q6tOq9UuWLBgxIgRZWVlbgkAAACgQUV9SG2Phfft72Dz\nDo8oQjuLVz85u8poKnd9kAUFUMCWGxK7kydP9u/ff9++fc7euGfPnvHjxzuSCAIAAHgXRu1U\nqRGGo7hZKrmfyFZjhiI+Za0rL2StkNuBldTELiMj49577y0uLnbt9u3bt69cuVJiDAAAAJ7i\n+JtQdSTTbZpC9FL+MXP2bknJ2c3czlJAhOESnyY1sXvxxRclroR49913DQbxGj8AAADez/Hc\nTtNf1mKweOO/vjZW5kia9V5w9Cxl9KH8l6V0Ao2dpMTuxIkTW7ZssTvZu3fv11577bvvvtNo\nNNVvCQwMXLlyZWhoqPVMXl7etm3bpIQBAADQWHSbqvBrLvJC1mIQTi/V8yLrKxxkCg14kEyZ\nVPQRlX0rIUBo3CQldps2bbI9jIyM3LRp0/HjxxcvXjx+/HiVSmQNNsuyM2fO3Lhxo+3J7du3\nSwkDAADAszSJMobRybmTdbbklEzCbBUrNj2vIpu/+L3Lk+3kpdovBEFpMI2mABS3812SErvd\nu3dbP3Mct3HjxkceecSRG4cNG9anTx/rYUZGhpQwAAAAPEz73/CAvqEB97Nsfp1tg9qzHcaK\nT7bL/N1046TFtRCM5juLKnYXazcS6+daD9AESErscnJyrJ/vueeeIUOGOH7voEGDrJ8zMzOl\nhAEAAOBhldtlXCrLFAYo33akebuH5OK16wRK+dRgKHFxsp3J0p8EDotkfZmkxO7GjRvWzz17\n9nTq3vDwcOvnrKwsKWEAAAB4WMSbpOhIgeMqDG850pxhKW6mQh4oMtnOWCakrDaQtN0DkNv5\nLEmJXUBAgPVzXl6eU/faJnMKhfiINAAAQOPAqKntCWq5keebOXiHMozt/jfxx1/BGUvmDqmZ\nGXI73yQpsYuMjLR+3rt3r1ardfBGs9l86NAh0X4AAAAaJTaInNzgK7KvLPpu8fYXNxjKMqVW\npLuZ2/GlKG7nOyQldr1739ogLzMzc+LEiQ5WpFuwYMH58+eth7GxsVLCAAAA8B5O5XZdnlb4\ntxB5Icub6exyvcUgdT/3G0cvUUYi5b8usR9oLCQldqNGjbI9/Omnnzp16rRixYq0tDRBEPlZ\nLCkp2bx5c2Ji4vvvv297fuTIkVLCAAAAaKQ4JZPwooqV3zpTZM7ZUPT629fvmXEk7uGn7v/q\n5zUW3sV1skTmsICRZPyLij6gsg3uiBe8HSOagTlIp9PFxMRcvXq1+qWgoCCdTmcy3ay02KNH\nj/T09NLS0uotQ0JCLl++HBYW5nIYHrFly5ZJkyaVlJR4OhAAAPBGTk1xy9hqSt1gJKJ0w6kl\neQ+3V/bu4zc6mIu6ajq3t/zzuN6xX33ws0KudCEMhWxfaMADJvMgRbefiA12oQdoXCSN2KnV\n6n//+9+il8rKyqxZHRGdPn1aNKsjovnz5ze6rA4AAKBOHJvFstccadn2AXl4PGcRzKsLnr0z\ncOJLUZuGBE7q4TfiweCX/tn8wF8nL6/a8KFrMRjNdxVXbC+q2IqszkdI3St2woQJ8+bNc/n2\nsWPHzp07V2IMAAAAXobXdPkiIrBHsN8Mh5ozFD9LmSlPKrXkPRwy3/ZKIBf+UPCr321d73Io\nRvNgIjkWyfoIqYkdES1atOhf//qXCyVLJk+evGHDBpZ1QwwAAABeROCpeCXDlCtl25Ryh7bN\nVAQxbP+slvIuCsZ+Q862yh6ZOekms+v7yFZBbucL3JNUvf766ydOnHjooYc4TqyOdjU9evTY\nsmXLunXrlEpXZgwAAAB4NUZGzdcSG1Chf9NoutvBmyI6qg1CZfXzBl7LMfIbh90QF3K7Js+J\nJdm16969++bNm69du7Zp06akpKRjx45lZ2dbK9vJZLKwsLCEhIT+/fuPGjWqf//+7vpeAAAA\nb6TqSx2uBnAhFQ7nUr3jBuQYU/NMl6PkHWzPn9Bu7ajse/5zU3B7mX9LkdooTsk9YtYkyoiv\nJNaPSGpv4G0krYqtk8lkKisrUyqVtntUNA1YFQsAAA5yfJxs1j8mHd979oXIb8NlrarOJFVu\n+qxg1pyo77uphvg1ZwYsUsvUUrMxlrkeGTWG/O+jZu9J7Aq8jdtG7ETJ5XLbPWEBAACgFkte\nX/W8ccpr+3q3U/QK4prlmC4Um69NCv+om2oIEWmvC+fWGBNekDaLibGEBd5H+gukP0nKWAp6\n0j2hg3dwf2KXk5Nz+PDh48ePnz9/vri4uLS0VK/XX7x4seqqTqdbt27dhAkTUOIEAAB8hCZR\n5uCgnVrl99l736dcPP3b54euJF/r5z+mm+quQO7WEEnuEXNIZ7bNSHktndRB4Mp1H4T6jyF1\nH/If7no/4JXc+Sp206ZNa9as2bVrF8/b70ln/ZbS0tKQkBA/P7958+a9+uqrcrmEH02PwqtY\nAABwilMLFwQLHXtXX/yXyJ4TLEd9FqhCuzi0WrEmCtkuk+WOqP6BUjoBL+SeVbFpaWlDhw59\n9NFHd+7cWT2rq06r1S5YsGDEiBFlZWVuCQAAAMD7yblTRA6NpzAcJbygVIaKbSNroTMfGwzF\nksZljOZhgqDGItmmxw2J3cmTJ/v3779v3z5nb9yzZ8/48eMdSQQBAAAaN75U0+6l8MBEtWK9\ng3coQ5j42UpG7EFtKBGSVxgEl7eQtYHcromRmthlZGTce++9xcXFrt2+ffv2lStXSowBAADA\n25lyqGQ1ER+ofo1hxPfYrC6sG9dpvHj9/6Jzlks/GN0SGnK7pkRqYvfiiy8WFRVJ6eHdd981\nGAwSwwAAAPBqym4U/hrJWpRp1wiCE9u2tntQHtVffKXjlV9MeUfdk5PdzO0Eg4NvisFrSUrs\nTpw4sWXLFruTvXv3fu2117777juNRlP9lsDAwJUrV4aGhlrP5OXlbdu2TUoYAAAAjUD4fGqf\nGtL7UefuYqj7DIV/C7HadQKlrDJWXndPKpaflE9Zw+jGQrf0Bp4iKbHbtGmT7WFkZOSmTZuO\nHz++ePHi8ePHq1T2u90REcuyM2fO3Lhxo+3J7dsd2kcPAH7bBNQAACAASURBVACgEWOUxLpS\nrl+mYhJeVHFKkdzOrBfOLNVbDNJzOz40YDjpDlLhu1T2jeTewGMkJXa7d++2fuY4buPGjY88\n8ogjNw4bNqxPnz7Ww4yMDClhAAAANCKaRKeLyAa2Yrv9TXyyXXkWf+4z6ZPt2Ar9u0QyUsaR\neojk3sBjJCV2OTk51s/33HPPkCFO/CgMGjTI+jkzM1NKGAAAAI2LC7ldiztkrYaJ33X9oDl7\nt9TJdgbTiOLKH/Py95O8lcSuwIMkJXY3btywfu7Zs6dT99puNZaVlSUlDAAAAF/QZbIytLP4\ng/vCekPZFanlwwymBwQhEItkGzVJW4oFBARYl8Tm5eU5da9tMqdQiA8v15OtW7euWbOmzmb/\n+Mc/evXq1QDxAACAD9IkynKPmGRcitkS5+AtLEfxf1f++YbeVG4/qY4306kPDQP/pZIHii2z\ncFLuEbMLY4rgDSSN2EVGRlo/7927V6vVOnij2Ww+dOiQaD8N4OrVqw35dQAAACKMaWEB94UH\nDuLYdMdvUoWzCX8Xr1qsL+TPLDMIbqr6j3G7RkpSYte7d2/r58zMzIkTJzpYkW7BggXnz5+3\nHsbGxkoJw1l48wsAAJ5X8atCtpchbZD6ZafuC+/OtX9YfKf1whTLlc0mdwRHdKu4nQnF7RoR\nSYndqFGjbA9/+umnTp06rVixIi0tTRBEfghKSko2b96cmJj4/vvv254fOXKklDCchRE7AADw\nvLAXSNWb/AaX6xc5e2uHRxQRPTjRS5c3GQvOuGOvMSIiyksqpuzRVPC2uzqE+saIZmAO0ul0\nMTExonlSUFCQTqczmW7+3tCjR4/09PTSUpFNVEJCQi5fvhwWFuZyGE4pLS2dOHFi1ecxY8YM\nGzasppaRkZGipfiqbNmyZdKkSSUlJe4PEQAAfISlgLjw3COu5GHmSuHPeTpdvshDXB7ADFik\nVkdKn2wnhAcOknNHiRhq8TUFPSm5Q6h3kkbs1Gr1v//9b9FLZWVl1qyOiE6fPi2a1RHR/Pnz\nGyyro9vfw8bFxbWuWS1ZHQAAgBtwEUSMa8sUZP5Mz7kqViGSvZkqhDPL9Lwb5sgxFbr5JHCk\n6ECq/tK7gwYgda/YCRMmzJs3z+Xbx44dO3fuXIkxOMV2fLF169YN+dUAAACiXMvtAtuwXSaK\nl5Uovcz/9ZUb9mE3mO8v0X6bf+MQKTpK7w0agBsWMy9atCgwMPCtt94yGp2rfD158uTVq1ez\nrNTk0inWxE6lUkVGRqampiYnJ+fk5DAMExkZ2bNnzy5dujRkPAAAAC5rNUxWetmSs09kdO7q\nH+bgDlzLIVIf9HrTI4QCKI2HpDl2tlJSUhYsWLB161aLpe65Aj169PjnP/85evRot3y1U+bP\nn3/27FkiatasWceOHQ8fPmzXICYmZvbs2W3atKm9H8yxAwAA93KtwghvoqS3dGXpImVOWDkl\nvq0ObOu2ARTkdt7PbYldlWvXrm3atCkpKenYsWPZ2dnWynYymSwsLCwhIaF///6jRo3q399j\nr+onTpxY02w/K5VK9Y9//KNbt25254uLi0+cOFH1+dixY0uWLCksLKyXKAEAwCflJRWxTImF\nd26mkDZPODxPZ9aKPND9opjE99RyPzdULa6C3M7LuTmxs2MymcrKypRKZUBAQP19i+PKy8uf\nfNKhRT2hoaErVqywC/vEiRPTp0+3Hl6/ft12t1wAAABJKrZasmbxgqaw/ICzk+DzT5pPLTGI\n1puL7C3r+ZKS3JbaVeV2vPRp+lAf6jfvlsvltnvC1quKiorff/9d9JJCoRgzZgxVK00cHx8/\nZsyYmJgYnU6XnJz85ZdfWgfziouLf/3118cff9y2fcuWLZ9//vmqz+fPn1+3bp37/zMAAMBH\nCVT0H47N4ihLrVivMz7j1M2RvWTtHuTTfxGpTpx/wpz+G9tulHhNYxfkHSmNip5I6kQKd331\nJNST+h2xa0i5ubnTpk0TvRQUFPTNN98Q0ZkzZ3777beqkyqVavbs2bbb1Kanp8+ZM4fnb05T\naNGixerVq2v6OsyxAwAANzOmUUYPrf6Jct37ghDk7N0CTycX6wvOisx0Z1jq/YYqvLt4TWNn\nvycs4B6F7AARQy02UNDjdd8BDci33pQnJCQkJCTUdLVdu3Z9+/ZNSkqqOrx27VpZWVlQkNP/\ntAAAAFyh6ETtr/jJospcWkXBsBQ3W/nnG3pDkf1CCoGns8sNA95TKcOkvz9lKg1zFdyfpGhN\nqh6SewM3cyKxmzJlSj0F4T3vNFu3bm1N7IiouLgYiR0AADQcWRQRaRJlrq2QVQQxPV5UHn1b\nJ1S721AqnF5q6LdQzUge0jGYRpXo1hnL7o7s0FJqX+BuTvz1rl+/vp6C8J7EjuNuG6Zu4Bp7\nAAAAEoV0YmMeV/z1tUhl2ZI0/uL3xpgnxWsaO0VvfJxQ3M4rNZ2/D41G88svv9TSQKfTvffe\ne9bDO+64Y8SIEXZtMjMzbQ9DQ0PdGCEAAICDXB60I6I298tL0/nrB0Vuz/jNFNyR1fR329Mf\nuZ238aG/DJVKdf78eesOtlqt9r777mOYW+u/MzMzjx49aj1s27atl1RpAQAAHyQlt4t9VlGe\nYanIrrY+UqCUVcaAllxAtNvKnyC38yo+9KqRYZgePW5N80xLS1uyZElWVpbZbC4qKtqzZ8/C\nhQttt824++67PREmAADALTIumWWcLobPKZkec9UytUj2ZjEIpz/UmfXurInxvwS0idTZaNR8\nK8UeO3bssWPHrIcHDhw4cOCAaEuNRnP//fc3VFwAAADVCZqYFULuawbziJLKH5292b85E/s3\nxZllhuqXKq8L59ca459XuiPIm/KOaKNaTyNlHIW/5sZuwVlOJHZHjhypvzgaRrdu3SZMmPDd\nd9/V3iwwMPDNN9+0LXEHAADQ0HgdFa9mGINKvkUh22U0D3O2A80AWclFS+Z2kfe51/80h8Rw\nrYe7bXwnxP9hKttJxJC8LQWNd1e34Cwn/kY9uMGrGz3xxBMhISHr1q0zGER+iSGi2NjYOXPm\nREZGNnBgAAAAt2H9qMUGyrq7rPIto/ke1/ro/JSyNF0oSRWpWpz6lSGoDRsS455JWTrTFKV8\nF8miSNHRLR2Ca5rOzhNOKS0t/eOPP06dOpWVlVVRUaFSqcLCwmJjYwcNGhQfH+9ID9h5AgAA\nGgJfTmygy6soiMhQIhyepzMUizzulWHswMUqRaB7FlKoFV8ZzXc169feLb2Ba3w0sZMOiR0A\nADQkKbld0TnL8ff0gv2GFERE4d253m+oGLeupcQiWQ+q9z/6rKysc+fOFRcXE5FGo2nTpk2H\nDh3q+0sBAACaGCnVT8JiuY7jFGnfi1QtLkyxXPrR2Okxd04rRwEUD5L0515eXp6Tk2MwGEQ3\nYP3uu+/efffdc+fO2Z2PiYl5+umn586dq1KppHw7AAAAOKj9Q/LSK3z+MZHU8MpmU3B7NrKP\nO1Mx5Hae4srYq9lsXrVq1Z133hkSEtK1a9fZs2dXbzNz5szHH3+8elZHRKmpqfPnz4+Pjz9z\n5owL3w4AAOCbJKVKDMXNUPhFiU2nE+jsaqM2z81Ts6S8OwaXOZ3YnTlzpkePHs8999yBAwd4\nXux1PdHSpUtXr15dez9paWnDhg0TzfwAAABAlCZRppJvUsgOuXCvzI/pMVfFKUVyO3OlcOYj\nvcXg7twuyUC5z1HRh+7tFmrhXGJ38uTJu+++u/ZsrKCgYOHChY70VlBQMH78eLMZGT0AAIAD\nBDNdnxTiPyHYbzLDlLnQQWBrNvZZ8el0ZZn8uc9EJuFJEeI3gUpWUf4rVO50gWVwjROJnVar\nffTRR4uKimpv9vXXX5eXlzvY57lz55YvX+54DAAAAL6LkRExRMQxV5Wy3a710XyQLPpu8Ve6\n1w+as/e6c7RFbxxHxBAXQlyUG7uFWjiR2L333nvp6el1Nvv999+rnwwMDLzrrruGDx/u7+9v\ndwmJHQAAgKOilpHfUGq7T2962OU+uk5RBrUXTwAurDOUXRGfZ+UCvemxMt2KG8WHyG+wu/qE\n2jma2BmNxk8//dTuJMdxcXFxI0aMsJ6pqKj473//a9csISEhNTV17969O3bsuHHjxvDhw22v\nXr58+c8//3Q+cgAAAN/DBlHrPaQeJKkPGfWcq5SL1SXmTXT6I4Op3G2T7bSGv1n4jlhI0WAc\nTex+//33goIC2zMajebw4cPJycnz58+3ntyzZ4/ReNsbeoZhvvnmm+bNm1cdqtXqLVu2REdH\n27bZu3evK7EDAAD4KonFRFThbPzzStG6xLoC/uxqg2g1YymQ2zUMRxO7AwcO2B5yHLdv376+\nffvaNUtKSrI7c99993Xv3t32jEqlmjRpku2ZkydPOhgGAAAAVJGY20XEce3HyEUv3ThpSd9i\nktK5KOR2DcDRxO7EiRO2h2PHjo2Jiane7NSpU3ZnHnzwwerNHnjgAdvDCxcuOBgGAAAAuEuH\nsYqIBE700qUfjYXJFrd/I3K7+uZoYpeRkWF7+NRTT4k2qz72duedd1Zv1qJFC9vDqg3HAAAA\nwCkSB+0YluJnK9XNRCbbCTyd+cSgu+HuN7JEuUeMlP8yXR1JlX+4vXNwNLErLS21PWzbtm31\nNteuXcvLy7M9ExISEhsbW71lVNRty57tOgcAAAAHafoLAap/+is/cO12eQCT8IKKFcsPTRXC\nmWUGwd1DbMF+U6joP1S5nczXxFsUvktZQ+naU8RXuvm7fYCjmX5l5W1/uK1atare5vjx43Zn\nevfuzTAivweUld1WVtFicf9gLwAAgE/IHhmg2k0kN5qHmSw9XegguAPbZaLy/DpD9Uull/i/\nvjZ0naKUHOUtBvMohfwAR9eKLzUzmETSRk2rE6TdR4yMWnwl3kVmInHh5DeMwua4MbCmwdHE\nrkWLFpmZmdZDk0lkTqXdAgsi6t27t2hvWVlZtochISEOhgEAAAC3CXqKKnfzQgDLXCdyJbEj\nolbDZSWX+Wv/FXm4Z+00B3dkWwwWX2bhAr1xnN44jognEn/Pqy9SKmQhJKjzj4i0YZjSqOAk\nIqqx6HHFVip8j7hIiniLVC7+gTRejiZ2HTt2tE3ssrKymjVrZtdm//79dmdqSuwuXbpkexgc\nHOxgGAAAAHCb4MlkzmGDJxmOa6R0EztVXp5lKc8QSbbOf2EKassFtHJ6f/lasTXNByup/IaI\niMSX5bJMqYVvwzL52rzI8kyRAT8/ZVqQ+jARUdhL4t+cO42MF0nRkTSfuRC3l3P0Lyk+Pt72\ncM+ePXYNrl69Wn3lRJ8+fUR7+/zzz20PO3fu7GAYAAAAYC98PsmiJS6kYBVMwgsquZ/IBCqL\nXjj9od6kdVvVYseIjxFa+NY3yi7llZaV6/9Rw40MLzQngbuR0iz3iLn6/5HuKGn3U+U+8bvN\neZTeg67eT6Vfu+O/oqE5mtgNGzbM9nDZsmVardb2zMcff2w3Va59+/bt27ev3tWpU6d27dpl\ne6amgT0AAABoMP4apvtzChJJ7agyV0hZZaQGTu3qIJ7Iag2z80uzcku1Fl4kCSEiY2UQL4Qa\ndVGiaV/hqatkOEOV28iUIf61xSspcyBljyFTlngDj3I0ux8yZIi/v791CUV2dvbdd9+9YcOG\nDh068Dy/fv36jz/+2O6WkSNHVu/nr7/+Gjt2rN3JwYOxhRwAAIBUmkSZxEJxkb1lbR/gM7aK\nvAbNP27O/J1t84DbJtvVsxqHrooqqt461lTJxWDh27JsfnlWpPaSyB9mkPqCn/IwEZHGGze7\ndzSx8/f3f+aZZz755BPrmaSkpI4dOzZv3rysrMxuzWwV2z1kLRbLW2+9deTIkYMHDxoMt627\nadmy5d133+1S8AAAAHAb6bld5wmK0st88QWRghWp3xqDOrChXcRrGjc24pmfydL/RlkaEVEN\n45MCqXi+BcveyD0eTiS2qlfaO3GJnJgIOWfOHLVabXfy+vXrolldYGDg0KFDrYdms3nRokW7\nd++2y+qIaNq0aSzr3vmYAAAAvo4hF4vAMRwlvKBUhYo8mgWeziw16IvdX7XYK4m9kyYq1/0r\nvywzt0Rb0yxAz3Iio2rXrt3ixYsdbPzCCy/4+/s70ucrr7zieAwAAABQO03f4hD/caEBY2sa\nc6qTMphJmKNkxQbmDKVC8lIDj/qz3sq5obLnn39+5syZdTbr2rXr66+/XmcztVr95ZdfVh8F\nBAAAANddn6KSb1bIdvspV7jcR0gnttMEheil4ov8pe+NLvcM9cq5xI5hmJUrVy5evNjPz6+m\nNl26dPntt9/qHK4LCgr65ZdfsGwCAADAzSKXEOtntnQxmQdK6abtA/Ko/uLTxdK3mvKS3L3X\nGLiDK5PbXnvttdTU1DfffDM+Pp7jbg7Usizbq1evJUuWHD9+vF27drXcLpPJJkyYcOHCBbsS\nKgAAAOAGii4UvU3W7aTJ0ktSPwzFzVT4txSbaiZQympjZY53lT8BImIEQdLfCs/zhYWFPM+H\nhoYqFOJjtkRkMpmmTZvWvHnzLl26jBo1KiwsTMqXeoMtW7ZMmjSppKTE04EAAADUSOIKWSKq\nvC4cmacz60WyhYBoNvEdFacSX2Tgszy7KlZqYuezkNgBAECjID23u/6nOfkT+6IWVZoPlMU/\nr5TYfxPTaMqdAAAAgA9qPlDWerh4snL9T/PVPzDZzosgsQMAAGjK3DKAFPO0MiRGPGf460tD\n8UUfqWzXCCCxAwAAaOI0iTKG0cu4FJd7YDlKeF6pCBSZTsdbKHmZwVSOmV1eAYkdAABAU2dI\nCQsYFOY/nGXyXO5DFc7G/13JiCUO+kL+zDKDgGE7L4DEDgAAoKkrXSfnzrDsjUDVAindhHfn\nOowVr4BRmGK5/BOqFnseEjsAAICmrtl7pIwj/xHl+rcl9tThYXlkH/FJe1d+NhWcxl5jHobE\nDgAAoKljlNR6D7XaxgvNJXdFcTMUflEik+0Ens4sN2jzMNnOk5DYAQAA+AAugty0QlbmzyTM\nUbEKkdzOXCmc+UjPG5HbeQwSOwAAAB/iltwuqA3bdbL4ZLuyTP7CV5hs5zFI7AAAAHyLW3K7\n6KGy6LvE+8nebc7Zh6rFnoHEDgAAAFzR9RllUHvxROL8F4aydJQ/8QAkdgAAAD5Hkyjj2By1\ncr2UTlg59ZijlItWLTbR6Y/0pgpMtmtoSOwAAAB8T/nmZqG9gtXTFbL9UrpRR7BxM8WrFutu\nCGdXGgipXcNCYgcAAOB7GIYsRUS8n3K5xJ6a9eTajZaLXrpxynLlF5PE/sEpbpg+WeXUqVN/\n/PFHcnJyYWGhXq936t69e/e6KwwAAACo2/+zd9+BTZT/H8Cfy+jeM9BCmbZQmS3DssqQLWKR\n4SxDREFBRKAigiAIKLLXV0CGA0FBW1SWlD0KtKWUUttCFy0the6Vfb8/4i/GS5omuaQZfb/+\nujx3z3Of5NL207tnuLxIPN4mXM/KB8vYN9Zhol1ltrz0jobZie8fEbu14/h04bI/C+jCCIld\nZmbmzJkzL168yL4pAAAAaCKC/xFC/H1J8XW2I1gpDun2nv21JcL6p8wBE7ScpG4TRaxxsPfC\nQ8KmwPZTTkxMDAsLQ1YHAADQnPFdqW7z7ClN94vEVfTtTSIa8580CVaJnVAojIqKqqmpMVY0\nAAAA0MSMMq0dIcS9Ayf4dXuNuyqy5Bk/YtbipsAqsdu/f39+fr6xQgEAAACzMFZuFzSC13KA\n5oEUeSckjy7hrp3JsUrsYmNjjRUHAAAA2IDO0/kurRqYtXivuOYhZi02LVYZekpKCqMkPDx8\nwYIFISEh/v7+HA66SQIAAFgHQV9e6a1LzvbbK2r3s0kPuA5Uj/kO1z6pl9Yzp7CTiejkjcLn\nVjvyHDXMaQxGwSqxKy0tVX05YsSIEydOUBSuFgAAgLWpPODtMoMQmYtDcI3wUzYtObWgusy2\nS96gYXbiuiI67Rtxt3mau+IBe6xuqrm4uKi+XL16NbI6AAAAq+Q0mHBdCSFcTg77xvzCeUGj\nNHe2K74uzTuBWYtNhVVi17JlS+U2RVGhoaGs4wEAAABz4Lcmgm9Iy58q6741SnvBr9p5hmiY\nl1hMC8/tu5OfXGaUswADq8Ru6NChym2apjHvCQAAgBVznUjcJhtrhCzFJd3m2dt7/vsor1Dy\n99risbPyWn78sG+f2f5DXgu7kHDGKOcCJVaJ3fTp01VHSKiPpQAAAACrY6zczt6D6vqePcUh\nhJBCyd+riob789utDri6N+jp+sA7ncrGvj7/pVMXjxvlXKDAKrHr2rVrTEyM8uWKFStoWq2f\nJAAAADRXXp25HafYEUJ+Klsa7vTCNO8tLfkhXIrnw2s9zmPRZM8VH376/t/f11fnYhoU42A7\nI8nKlSunT5+u2L506dKsWbOqq6tZRwUAAADmZKybdoSQtmP5nuGSe/Xnh7u9y9gV6Tq1RlJ6\n7tjNqx/XX5xbl3lIXFuIO0SssLpsp0+fTk1N7dSpU+fOne/du0cI2b179++//x4ZGdmpUycn\nJycd21mwYAGbMAAAAMDoBH15xdelFCWiaXazk1DE/+Vq2S9SL14gYw+fcnDn+lfIHhNC6p/Q\nOXGSnDiJaxCnRQRPEMF19MGEuHpjldj9/PPPe/bsYRQWFRUdOnRIr3aQ2AEAAFggR7vvXBw+\nLau5IJMHsWnHz9+by+GWS4uc7TxUy6W0uEr2xJ3rq1pYnSevzhNnHiIugVTLAfyWA3iqIzBA\nO+TCAAAAoEnVj+5O07mcQnentwhh1QfO0cGpX1hkfDXzZtClmh8cOK5t7LprrFVTQGceEl94\nr+7WamHBOam0Fk9pG2e0J+gAAABgU1wnEcctRJgskgwnFK2+jIRelr2/btysQbwyuxFuc7x5\ngTXysss1h46Wf/62zy4upXkqYwVaTkrvykrvyu59S3y78vwjuP7hXK497uFphsQOAAAANKF4\npMV3RF7r6tC99rqUZWOhHbsd23425qv3FqQ/a0c5iul6P7s27/juCXMaq2MLtJSUJElLkqRp\nfOL9LFfwHNe/Nw8ZHgMSOwAAAGiAXUcjNtatU9iJb69VVJXdz88M8AsU+ARWZskKL0mLr8qk\n9XrcD5RLyJNk2ZNk2d/7Jb5hXP++XJ9uPI6GRS6aI1aJ3bRp0/r372+sUAAAAMAyKUbIGqUp\nDzev8Gf7/rMdzPUI5naKJk9TpY+vyx7flMmEemR4kjr60SXpo0tSvovYtwfXvy/XtzuPat7D\nByhMKWyY2NjY6OjoiooKcwcCAADQRIyV2zVEJqKf3JY9uiB9miqjDTqVvRdH0Jsj6MvzeIZL\nzPSQ1ohTABoAj2IBAADAInDtKUEfnqAPT1pLlyRJi6/JnqbIaH3G44rK5Hkn5XknpY4+HL9w\nTsuBfLe2zesOHhI7AAAA0InigSyPky6VdzLpiXjOihns+MJy+ePrsuIEaUWmXK9hufVP/8nw\nXAIp/z68Fv35zoJmMcwCiR0AAADohhYK2n9GSr+qqP1RKJnQBCd08OQEjeIEjeILS+WPb8iK\nE6QVGfrNqFdTQNcUSB4clSimO24xgOvgacv38NDHzkDoYwcAAM1O/RWSN5AQuVzu96Q6g6Zd\nmj6EmgK6+Lqk6Kq0rsiQBIbiEPeOHEEfXot+PDs3k9zDs44+di+88ILqy8DAwJ07dxJCpk2b\nxj6Iffv2sW8EAAAATMuxH/H6kFQerKzdZZasjhDiEkh1eNmuw8t2NQ/lxQnSR5ek9SV6ZHi0\nnFRkyCsyxBnfi706c1sO5PqF83iOtvOUVtc7dhT1n/ccEhKSnp6uXm4Ya7xriDt2AADQHNEi\nIq8iXF9Tj5DVES0nlVmy4uvS4msyUaUh6QRHOd1xLx7XwQhZjXXcsQMAAAAglD3h+po7iH9R\nnH8mwwt+g7Cc7vievcS3O7fFQOue7hiJHQAAAOjNiFMWG4Uyw+s0lTy9I318Xfb4hkwm0iPD\nk4no4gRpcYKU5yz268n178v17cajrC3DQ2IHAAAAhrC03E6BwyN+PXl+PXmhb9FP78qKLsoe\nJ0r1mu5YWvvPghb2XhKzT3esLyR2AAAAYCDLzO0UOHaUIsOT1tqxnO7YwZvj34vTcgDfrZ2l\nT5Wia2KXm5ur+pLP5ys2rl+/btyAAAAAwKrQjnYHhZLJNO1g7kg0U053LKmmH9+QFV6S6Dvd\nsbD0P9Mdt+zHd2phoXfwdE3sgoKCNJb36dPHeMEAAACAVZEWCQLeJLV/8UR3quu/Nnc0jeC7\nUoFDeYFDeUaZ7ti/Dy9gIN/Rz7IyPDyKBQAAAENRPCK6Swhxst9bK1wopwXmDkgnDt7/LGih\nmO64+Kq0Vs/pjhUZXvavEsV0x4IInr27RWR4WHnCQJjHDgAAgBBCak+RJ58+fbJfKnvG3KEY\nTjHdcdFlad1jAxe0eOh95br0UF7tPU+BS/fu3d97772OHTsaPc7GI0FiZxgkdgAAAP9PTgjH\nYkdR6I6Wk4pMWdE16eNrMnG1HgnSbxVr/6zc1M/llXb2YWK6Pq3+fDodf/jwYcbCXU0Aj2IB\nAACAJQ6x7BGyOqI4xDOE6xnC7RT9z4IWj67IJI1leOnCi39WbooR/NHOPkxRMtR1Znz13jfe\neCMrK8vXt0nnc7b0UbsAAAAATUwx3XFItH3kDqceC+1bDuBx7RvsQne++sAAlzeUWZ3CENcZ\n7vWtDx8+bPpg/wOJHQAAABiHeZdJNQXFdMddZtsP+caxx0J7QR8epfYWiySZjKxOob19r/T0\n9KaIUoWtXQAAAAAwIxt4IKvRv9Md19mVJP5numMeZSelRepVJHS9nZ1bU8fZxOcDAAAA20ZR\n1W6Oc3jcDHMHYhI8J6rlAH7PRQ6DdzmFvmXvEcxpZx+WUn+KcZiEFt4TXmj66X5xxw4AAACM\nR1ro7zOQSLL5vMTS6kuE8M0dkKkopzv2uPvByNnhp0CbQQAAIABJREFUp6t2Pu/2DkUoQoiY\nFu4vnefXwS0qKqqJo0JiBwAAAMbDa0ns2hNJNod6yuXky+TtzR2QyQU/23HfVz+/u+yN+MK9\n7ezDJLQwQ3g1MNj3eOxxOzu7Jg4GiR0AAAAYEUVafEuerub6rZXdcDZ3ME0kss/whKMZpy4d\n//tBmk975096Ths9ejSfb4a7lUjsAAAAwKh4gUSwkxBCiA2OomiIm4vHxFFvEHMPDcbgCQAA\nADAJ25v9xPIhsQMAAABTQW7XxIz/cRcWFl67du3WrVv37t0rLy+vrKwUCoWZmZmKvfX19fv2\n7ZsyZYqXl5fRTw0AAADQnBkzsTt69Og333zz119/yeXyho4Ri8Vz5sxZuHDhkiVLFi1aZJZ+\nhQAAANBk/pmymJIRmmvuWGyfcR7FZmVlDR48+OWXXz59+rSWrE6prq5u6dKlI0eOrKqqMkoA\nAAAAYLEEYQVeLsMc7Q6aOxDbZ4TELikpqU+fPufPn9e3Ynx8/OTJk3VJBAEAAMBayStJbk87\n7mU3x/lcTq65o7FxbBO73Nzc559/vry83LDqJ0+e3LFjB8sYAAAAwHJx3InXAkKIRNYNozZN\nje3n+8EHH5SVlbFpYdWqVSKRhqVzAQAAwEZ4x5AW++2ePS+TtzZ3KDaOVWKXmJgYGxvLKAwL\nC1u8ePFPP/0kEAjUq7i6uu7YscPT01NZ8vjx4xMnTrAJAwAAACwbl7hHE8LB7CemxiqxO3r0\nqOpLPz+/o0eP3rp1a+3atZMnT3ZwcNBwPg7n3XffPXLkiGrhyZMn2YQBAAAAAIRlYnf27Fnl\nNpfLPXLkSFRUlC4Vhw0bFh4ernyZm5vLJgwAAACwFrhpZ1KsErvCwkLl9tChQwcNGqR73f79\n+yu38/Ly2IQBAAAAVkSZ29nxrpo3EtvDKrF78uSJcrtHjx561fX29lZu5+fnswkDAAAArIug\nL8+OF+/lMsjLZTiPm2HucGwHq8TOxcVFuf348WO96qomc3Z2dmzCAAAAAGtDe3l/RAix453n\nUMXmDsZ2sErs/Pz8lNvnzp2rq6vTsaJUKr1y5YrGdgAAAKAZoEjL74hjf+L2ilf4UHMHYztY\nJXZhYWHK7by8vDfeeEPHGemWLl1679495cvQ0FA2YQAAAID1se9Ggi4SwTcEIyqMh1ViN3bs\nWNWXx44d69ix4/bt27OysmiaVj++oqLit99+69u377p161TLR40axSYMAAAAsE4U4TgrtgR9\neUjv2KM0ZmA6qq+vDw4OfvjwofouNze3+vp6iUSieNm9e/ecnJzKykr1Iz08PB48eODl5WVw\nGGYRGxsbHR1dUVFh7kAAAABsTfF1safzBKEkql78OiGUucPRj3nTU1Z37BwdHb/88kuNu6qq\nqpRZHSHk9u3bGrM6Qsgnn3xidVkdAAAAmI4geI89/3d3p+kuDp+ZOxYrw3at2ClTpixZssTg\n6hMmTPjwww9ZxgAAAAA2RfaEUHxCOdaLp5k7FCvDNrEjhKxevXrNmjUGTFkyderUH374gcMx\nQgwAAABgO3yWkzbJpMVu394d0PFOL8ZJqmJiYhITE1988UUul6vL8d27d4+Njd23b5+9vb1R\nAgAAAACbYh9K3F5TbCK3053RPqlnn332t99+e/To0dGjRxMSEm7evFlQUKCc2Y7H43l5eXXr\n1q1Pnz5jx47t06ePsc4LAAAANk+R2xVflxJC+NwEmTxYTnuYOyhLxGpUbKMkEklVVZW9vb3q\nGhW2AaNiAQAAzEBWIc8MITRdXb+uXvK6uaPRwLz3F017bj6fr7omLAAAAAArZes51GNCER4v\njUgaP7y5wUNrAAAAsB7eSwjFI5UHnJ/5tPqGuYOxPKwSu2PHjt28edM4cfB4AoEgODh40KBB\nfD7fKG0CAACAreE4EZ/PiPfHhLIX9CXk/zvegQKrxO7EiRN79uwxVigKHh4eixcvXrBgAdI7\nAAAA0Iz6d1YNQV8ecjsli5tDrqKi4uOPPx42bFhtba25YwEAAAAroFxn1tVhqbvzNA7nibkj\nMhuLS+wULl68+Morr5g7CgAAALAagp4PnB03OvK/93IeQogJJ/2wZBaa2BFCjh8/fuzYMXNH\nAQAAAFZC9oTwWhBCakUfEUKZOxrzMHli5+TkRFEGfrhff/21cYMBAAAAm+XYn7S9R/y3u/ec\n3mwXq2CV2O3evZum6dLS0hEjRqiWDx069NChQ8nJyWVlZbW1tUKhMCsr69SpU1OnTmUsKfv5\n55/TNE3TdF1d3bVr1+bPn6+6dOzVq1fv37/PJkIAAABoRjhOxHO24nadsuNds8J25YlHjx71\n69cvNzdX8fK5557bvXt3aGioluPfe++9X3/9VVnyww8/vPrqq8qXJ06cGDNmjDKqAwcOvPnm\nm2wiNBGsPAEAAGAVFGNmuZw8DvVYIutt6tOZN5tkdcdOLBaPHz9emdWFh4efO3dOS1ZHCGnZ\nsuUvv/wyZMgQZcn777//5Mm/o1dGjRo1depU5cukpCQ2EQIAAEAzp7h15+q4wNt1gLvT2xRV\nY+6ITIhVYvfjjz+qTlC8detWe3t7Lcf/c0oO56uvvlK+LCsr27Vrl+oB0dHRyu2ioiI2EQIA\nAAAQ4Q0Hfiwhch43iSaO5o7GhFgldvv27VNuOzs79+3bV8eKPXv2dHV1Vb6Mi4tT3at6z6+y\nspJNhAAAAADEoTdpdYbYhfDbbiE019zRmBCrx8AZGRmGn5j376mVD3MVVNecEIvFBp8CAAAA\n4B/Ow0jbVELxbHshMlZ37KqqqpTbtbW1ly9f1rFiSkpKeXm5xnYIIX///bdy293dnU2EAAAA\nAP+g/r2ppDZm1kYmNGaV2AUEBKi+fOedd3QZJSoUCmfPnq1aIhAIVF/u3LlTue3j48MmQgAA\nAICGKHI7O94Vb9dedrwr5g7HCFgldmFhYaov09LSevbs+cMPPwiFQo3H0zR98uTJfv36Xb16\nVbX8mWeeUWzU1tYuW7bswIEDyl1dunRhEyEAAACAFoI+xMv7PT43xctlMI9zz9zhsMWqj110\ndPThw4dVS3Jycl5//fX3338/KiqqXbt2AQEBAoGgqqqqoKAgPz8/Li4uOztbvZ0JEyYoNmbP\nnn3w4EHVXboPyAAAAADQm7ya2HUkorvE9WVpRWdzR8MWq8Ru5MiRgwcPPnfuHKO8vLx87969\nOjbi7+8/ZcoUxbZcLlfdFRQU1KtXLzYRAgAAAGjD9SQBx0jtSWIXKgjgWfugClaPYimK2rdv\nn7+/P5tGtm7d6uHhoXHX3LlzDV5nFgAAAEBXziMJvxWx/oXIWCV2hJCgoKDLly+3bdvWgLoU\nRW3btm3ixIka93bu3HnOnDnsogMAAADQm0p6J3e0O0iI1dzGY5vYEUI6dOhw+/btuXPncrl6\nzPjXvn37kydPNpS6tWrVKi4uTpd1LAAAAABMQdCX52S/191pho9rOJ9729zh6MQIiR0hxM3N\nbfPmzZmZmUuXLm3Tpo2283E4gwYN2r9//927d4cPH66xqdmzZycnJ7dv394osQEAAAAYgpa6\nua0lhHA5D+TEzdzR6ISiaePPyPfo0aMbN25kZ2dXVFRUVFTw+Xx3d3dvb++uXbv26NHDxcWl\noYo1NTVa9lqU2NjY6OhoXebtAwAAAGslfUSexBB+W+KzQsdxFebtomeSc7ds2XL8+PEGVLSW\nrA4AAACaBV5L0uKgYl0KQV8rGDNrnEexAAAAALbrnzk61MbMWlyeh8QOAAAAQA+K9I6iqn3d\nnnVxWElRInNH9C+jPYpNTk4+c+bMnTt3SktLG1pSrCHqUxwDAAAAWDL/titJ2QMXh89pYlcr\njDF3OP8wQmKXmZk5c+bMixcvsm8KAAAAwDrw2xCOG+E414lmmzuUf7FN7BITEyMjI2tqaowS\nDQAAAIB18JxHXCcRyQP/Dl6EEAsZV8EqsRMKhVFRUcjqAAAAoDnitSC8FopNxaAKs6d3rAZP\n7N+/Pz8/31ihAAAAAFg1s68zyyqxi42NNVYcAAAAAMASq7wyJSWFURIeHr5gwYKQkBB/f38O\nB3OpAAAAADQdVoldaWmp6ssRI0acOHGCoih2IQEAAACAIVjdVGOsALZ69WpkdQAAAADmwiqx\na9mypXKboqjQ0FDW8QAAAACAgVgldkOHDlVu0zSNeU8AAAAAzIhVYjd9+nTVERLqYykAAAAA\noMmwSuy6du0aE/Pv4mgrVqygaZp1SAAAAABgCLYzkqxcuXL69OmK7UuXLs2aNau6upp1VAAA\nAACgN1bTnZw+fTo1NbVTp06dO3e+d+8eIWT37t2///57ZGRkp06dnJycdGxnwYIFbMIAAAAA\nAEIIxebh6cyZM/fs2cM+CGt8gBsbGxsdHV1RUWHuQAAAAAD+gcUhAAAAAGwEEjsAAAAAG4HE\nDgAAAMBGILEDAAAAsBGsRsVOmzatf//+xgoFAAAAANhgldhFREREREQYKxQAAAAAYAOPYgEA\nAABsBBI7AAAAABth/sTuiy++WLp0qbmjAAAAALB6rPrYqSovL4+Pjy8oKNB9MYby8vJr167d\nuHFj6tSpxgoDAAAAoNkyQmJXVlYWExPz7bffymQy9q0BAAAAgGHYJnZlZWWDBw++c+eOUaIB\nAAAAAIOx7WM3f/58ZHUAAAAAloBVYnf//v2DBw8aKxQAAAAAYINVYnfkyBH2Ebi5uT333HPs\n2wEAAABo5lj1sbt7967qy7Zt23722Wd+fn5Xrlz54osv5HK5ovzzzz8fOHBgfn5+bm7ukSNH\nUlNTFeV2dnbHjx+PjIy0s7NjEwYAAAAAEJaJXUFBgXKboqg//vijU6dOhJCRI0cKhcL169cr\ndmVlZSlnqvvkk0+OHj362muvicVisVg8a9asGzdu+Pr6sgkDAAAAAAjLR7GFhYXK7ZCQEEVW\np/D8888rt+Pi4qRSqWKboqiXX355y5Ytipe5ubkffvghmxgAAAAAQMFoiZ1AIFDdFRYWptyu\nqKhIS0tT3fvWW285OTkptr///vvExEQ2YQAAAAAAYZnYcblc5XZVVZXqLm9v7zZt2ihfXr9+\nnVGxW7duypfff/89mzDAqqWlpVH/pXq7FwAAAHTHKrFzc3NTbmdnZ4tEItW9qjft/vrrL0Zd\n5dAKjXsBAAAAQF+sEruOHTsqt8vLyzds2KC6VzWxO3nyZHFxsfLl06dPlWNjCSH5+flswgAA\nAAAAwjKx69Wrl+rLJUuWjBo1auvWrYrHsgMGDFDuqqmpmThxYkpKSl1d3d27dydPnlxXV6fc\ny+fz2YQBAAAAAIRlYjdp0iRGycmTJ+fOnZuTk0MIiYiI8PPzU+66fPly9+7dnZ2du3TpEh8f\nr1orODiYTRgAAAAAQFgmdn369OnXr1+DTXM448aN06Wdzp07swkDAAAAAAjLxI4QsnfvXtUh\nFAzvvvsuh9P4KaZNm8YyDAAAAABgm9gFBwfHx8e3aNFC496ePXt+/PHH2luYMWNGREQEyzAA\nAAAAgG1iRwgJCwvLzMxcuXJlSEiI+t5Vq1atWrWKx9OwdhlFUbNmzdq1axf7GAAAAACAomna\niM0VFhZmZWX16NHD3d1dtbygoODIkSMZGRnZ2dklJSXe3t7h4eGvvvpq9+7djXj2phQbGxsd\nHV1RUWHuQKxeWlras88+q1oybNiwM2fOmCseAAAA66XhRhobAQEBAQEB6uWBgYFYExYAAADA\npIzwKBYAAAAALAESOwAAAAAbYbRHsSKR6MaNG6mpqU+fPq2vr9er7po1a4wVBgAAAECzZYTE\nrqamZtWqVTt37lSsJGYAK03s6urqQkNDKysrq6ura2trnZycXF1d3dzc2rdv36VLl+7du48c\nOZIxiMSI6uvrjxw5cvbs2cLCwoKCgoKCAi6X6+Xl1bJly+eee27AgAGjR4+2s7Mz1ulyc3Pj\n4uIuXbpUVFRUXFxcVFREUZSXl5efn1/Pnj179eo1duzYhma9AQAAgCZCs1NQUNCpUyfzxmAW\nv/32W6Pvy87Obty4cRcuXNCrZaFQyGhn/fr1qgfk5OTMnTvXw8ND+9kFAsGqVauEQiGbtymR\nSLZv396tW7dG3yyXyx0+fPgff/yh7ynu3r3LaGrYsGFsYgYAAGi2WPWxk8vlUVFR6enpbBqx\nYWKxOC4ubtCgQS+//PKTJ0+M0ubBgwe7du26ZcuWRmdaKS4uXrp0aVhY2J07dww716lTp7p1\n6zZnzpyUlJRGD5bJZKdPnx4zZkxkZGRaWpphZwQAAAA2WCV2P//8840bN4wVig07evTogAED\nHj58yLKd1atXR0dHV1dX614lLS1tyJAh9+7d0+tENE3Pnz9/5MiR+lYkhFy4cKF379779+/X\ntyIAAACwxCqx++mnn4wVh83LyMgYO3asRCIxuIVvvvlm6dKlBlQsLS0dPXp0XV2djsdLpdI3\n33xz06ZNBpxLoa6ubtq0aVbadRIAAMB6sRo8oX67zt3dPTo6Ojg4ODAwkMvlsmnc8lEUFR4e\nHhgYGBAQ4OHh8fjx40ePHhUWFqampspkMvXj79y5s379+kYXz9UoOTl53rx5qiWhoaETJ07s\n0aNHQECAnZ1dSUnJzZs3jx07dvPmTfXqeXl5a9euXblypS7nevPNNw8dOqRxF5/Pj4iI6NCh\ng7+/v1AofPToUUJCQk5OjsaDlyxZ4uzsPHfuXF1OCgAAAEbApoMeY9Blt27dnj59aqTOf5bu\nt99+c3d317jr4cOHy5cv1zhENCAgQC6Xa29ZffDE0qVL27dvr3zZvn37EydONFT9zz//FAgE\n6qf29PSUSCSNvq+GHqG2adPm22+/raqqUq9y586d6OhoDkfD3V8ej3fjxg3tZ8TgCQAAAGNh\nldh5e3ur/j2+ePGiscKyfFoSO4Xy8vKBAweq5zpXrlzR3rJ6Yufk5KTcfv7552tra7W3kJub\n6+/vr37qM2fOaK+Yk5Pj5uamXnHu3LkikUh73cTExDZt2qjXfeaZZ7TXRWIHAABgLKz62LVq\n1Uq5rXguyaY1G+Ph4fHLL794eXkxytXzmEYpu8cNGjQoLi5ONc/TKCgoaM+ePerlycnJ2isu\nXLhQfTLCTZs2bd68udEp8Xr27Hnjxo0ePXowyjMzMzGQAgAAoGmwSuzGjBmj3KZpWq/Rms2B\nr6/vK6+8wigsKioyrDVXV9eDBw86ODjocvDYsWPVJ5/Tfuq8vLxff/2VUfjuu+8y+vZp4evr\nGxsb6+fnxyhfvXq1xk6HAAAAYFysEru33npLNc/A1Cfq1LOrRuefa8iyZctat26t+/Fjx45l\nlJSXl2s5ftu2bYz0q1WrVhs3btT9jIoqW7ZsYRTm5+dfvnxZr3YAAADAAKwSuzZt2mzevFn5\ncvHixer9w5o5jV3WDODk5PTWW2/pVSUkJET3g6VSqfrT25UrV9rb2+t1UkLIpEmT1B/Iqt8L\nBAAAAKNjldgRQt5+++2vvvpKMbPJvXv3RowYkZmZaYzAbIRIJDJKOxMmTGh0DTEG9e59Wty+\nfZtxK9HV1XXKlCl6nVGBoqjo6GhG4dWrVw1oCgAAAPSixzx206ZNa2hXhw4dMjIyCCEXL14M\nDQ0NCQnp3Llzo338lfbt26d7GNbFWItrDR48WN8qes0jqP6odOzYsTr251M3duzYDz74QLVE\nMbefzU9tCAAAYF56JHY6jm2USqV3797Va+ynrSZ2+fn53333nVGaeu6554zSTkMuXbpkxDO2\na9eOz+erLrMhFArz8vLatWtncJsAAADQKLaPYkGjmpqanTt3Dho0yOAxsAyqExSbgvpMKHp1\n0WOgKEp9bGxlZaXBDQIAAIAuWC0pZmliYmIUi9aPGjXq3Xff1XIkTdMpKSnx8fHZ2dmlpaUy\nmczHxycgICAyMrJPnz48nn4fi1gszsnJuX//fkZGxt27d1NTU+/evWvEcSQuLi58Pt9YrWlU\nWlrKKHn33XcdHR0NbrC4uJhRgtlwAAAATM12ErukpCRFVteo6urqr7/+OikpSbWwoKCgoKAg\nISEhKChoyZIlGhcEY6ivrx8+fHhWVlZ+fr5cLjcwbh3oO2xCX1KpVH1e4gcPHhj3LOqnAAAA\nAOOykUexV69eXbNmjS5HSqXS5cuXM7I6VXl5eYsWLdJltjmxWHzmzJnc3FyTZnWEEH3vIOpL\n+/x2xoI7dgAAAKamR8Zw/fp108WhL6lUWlFRUVFRkZGRcf78ecWYXF0cOnTo/v37qiWenp4O\nDg7FxcU0TStKKisrt23btnTpUpZBUhQVEBBQUFDAsh1Ta5p7abhjBwAAYGp6JHZ9+vQxXRz6\nWrFiRUpKir61hELhn3/+qXzp4+OzfPnyoKAgQkhlZeXnn3+unITvxo0bhYWFAQEBBsTm5+fX\npUuXF198MSoq6vLly4bNBteUXFxcmuAsuGMHAABgarbTx04XN2/erK2tVb589dVXFVkdIcTd\n3X3mzJkLFy5U7j1//vxrr73WaJutW7fu3r17iApPT0+jR25SGgMuKyuzujcCAADQzDWvxI4x\nXTBj5atnnnnGxcWlpqZG8bLRoRhOTk7Z2dn+/v7GDbLp2dnZOTk51dXVqRaWlJQgsQMAALAu\nbBM7qVR6//79v//+e/z48VqO2bx5c4cOHUJDQzt06MDyjAoLFy5Unf+2pqbm/fffb7RWXl6e\nclsgEHh7e6vupSgqNDQ0ISFB8TInJ0d7a3w+3wayOgUvLy9GYvf48ePg4GBzxQMAAAAGMDyx\nO3fu3P79+48ePVpbW+vg4FBfX9/QkTKZ7KOPPlJsd+3a9fXXX58zZ47uC45p5ObmpvrSzs5O\nl1qq3bw03o5yd3dXbtfW1tI0TVGUoTFak9DQUMYgj3v37g0cONBc8QAAAIABDJnu5OHDh+PG\njRsyZMjBgwdVu6zp4s6dO4sWLQoNDT1x4oQBp2ZJNbHTOPuuarpJ07S+706d8sGuhYuIiGCU\nnD9/3hyBAAAAgOH0vmOXlJT0/PPPl5WVsTlrbm7u2LFj9+7dO3XqVDbt6EuvxI4QUlNTozpi\ntLi4+NSpU4rte/fu6XKbsNHnuRaiX79+jJJz585JpVLDptB78ODB5cuXVUvat2/fv39/w+MD\nAAAAHej3Z/vu3btDhw7VZfLeRsnl8unTp3t6er744ouq5TU1Naozkqiys7PT0pOvURKJRCqV\nKl9qXKTLwcGBEYzqy8LCwq1bt2pvgcFa7nv16dOHz+erdlssKSk5cuTIq6++akBrc+fOZVzE\nTZs2IbEDAAAwNT0SO4lE8sYbbxglq1Ogafqdd94ZMGCAl5eXsrCmpub777/XeLybmxubxI7P\n5/N4PGVuJxKJ1I9hFDLuybVr127t2rWK7Zs3b65fv177GVNSUq5cuWJwwE3JxcUlKirq8OHD\nqoXr1q2bMmUKh6Pf8/rExET11HzkyJFsQwQAAIDG6PE3e/Pmzbdv31Yv53K5w4YN01KRy+WO\nHz9e42CF4uJiHZcCMwpXV1fltsbRHoxCxuNaT0/PYf+vc+fOMplMy7nEYvGMGTPYxduk5s6d\nyyi5c+fOypUr9W1nxYoVjJLQ0FAMsAUAAGgCuiZ2MplM9Smkgr29/Zo1awoLC48fP66lLo/H\n+/XXX58+fXrp0qXw8HDG3n379gmFQt0jZkO1wxxjdg8FRmLn7Oxs2IkkEsnUqVMTExPVd6k+\nDrYoERERPXv2ZBR+/vnnR48e1b2RLVu2qH8ZYmJi2AYHAAAAOtA1sTtz5kx+fr5qSYsWLS5c\nuBATE6PjXG4cDqd///6XLl0aNGiQanlpaWlDneqMTvWOncaV70tLS5Xbnp6ehs3JkpeXN2LE\niEOHDmncq1y1zAJ9+eWXjOld5HL5pEmTvvrqK+VCug2haXrdunXz589nlLdv397yF1UDAACw\nDbr2sVMfBLBr1y4DVo91cHDYvHlzjx49VBOFa9euRUVFKbYFAkFcXJy+zeooKChIuZ7EkydP\nioqKWrRoodwrl8vv3r2rfNm2bVt928/Pz//f//63ceNGLbP6nTlz5vfffx87dqy+jTeBoUOH\nzps3b9OmTaqFcrl80aJF33333fLly8eMGcMYX0IIEQqF33///YYNG9LT0xm7uFzu/v37DRta\nCwAAAPrS9S/u1atXVV/26tVr3Lhxhp2yW7du48aNi42NVZYoF3swtWeffVZ1/ryEhATV0Rip\nqamqz2e7du2qvTWpVHrr1q2ampqSkpLk5OQrV65cuXJFLperHtO5c+f09HTVLJam6RdeeGHI\nkCGdO3cWCoVbt25VT5XMaM2aNX/99ZdqgquQmpr68ssvOzo6DhgwoH379r6+vhKJJD8//+HD\nh3fv3m1o+ptPP/0Ug2EBAACajK6JHeM5LMs1CXr16qWa2BUWFrJpTa/zOjs7K6cd/umnn3x8\nfLp06cLj8f7++++dO3cqj6QoqtH3WFtb26tXLy0HjBkz5qeffpowYcLp06cZu+Lj4+Pj4wkh\njNtjZufg4HDmzJlRo0ZpHChTX1+v/l4aMm/evGXLlhk1OgAAANBG18SOcUumffv2bM7arl07\nLY2bjoODw9ixY5WTetTV1X355Zcajxw6dKiPjw+bc82ZM2fz5s1cLnfRokV//fUX406eJRMI\nBBcuXBg3btyFCxcMa4HD4Sxbtmz58uXGDQwAAAC003XwhPYJ3vTFmPqE/cpdups8eXKHDh20\nH+Pn5zdt2jSDT9GrV6+LFy9u27aNy+USQoYOHbplyxaDWzMLNze306dPr1+/3sPDQ9+6oaGh\nV69eRVYHAADQ9HRN7Hx9fVVfsnx4WlRUpKVxk+LxeCtWrAgLC2voAMUsxKrjZ3XE4XD69ev3\n448/JiQkDBgwQHXXnDlz/vjjj06dOjGq+Pr66jv9b5Oxs7NbsGDB/fv358+fr+PA54iIiB9+\n+CE5OdmAUTUAAADAnq6PYv39/VWTubNnz7Lyc/CMAAAgAElEQVTpPsVYj0HHvMFYXF1dly1b\nlpKScvbs2ezs7NLSUolE4ubm1rFjx4iIiEGDBjGm/NCibdu2LVu2DAwMHDJkyIsvvqjljYwe\nPXrEiBFpaWl3796tqanx9fXt2rWrxifa9vb2jc4toosRI0ZUV1dXVVVV/j9vb299G/H29t6w\nYcPXX3998+bN33///datWyUlJY8fPy4pKbG3t/fy8vL29u7SpUu/fv0GDRr0zDPPGBBnaGio\nUd4vAAAAUDr+TZ00adLPP//8bzWKSk1NDQ0NNeCUlZWVbdu2VZ1Gbvz48b/++qsBTZlRbGxs\ndHS0ERdYAwAAAGBJ1+eAjLU+aZqeMWOGWCw24JQffPABY3JgLCQKAAAAwJ6uid3o0aMZDygT\nEhLGjBlTVVWl+8nkcvm8efP279/PKLfM2XoBAAAArIuufewEAsFLL7107Ngx1cK//vorJCRk\nzZo1U6ZMsbe311Kdpun4+PiFCxcmJyczdo0ePTogIECvoC2B7v3wAEALuVwukUj0rSUSiaqr\nqw04nb+/P5ZCAQAbpmsfO0JIVlZWaGioxl/B7u7u48aN6927d5cuXfz8/Nzc3DgcTnV1dVlZ\nWXp6elJSUlxcHGOKYwUul3vnzp3OnTuzehPm8OTJk9jY2LfeesvcgQBYt8zMzNTUVH1r1dXV\nubq66juoXCaTDRgwgOUUlQAAlkyPxI4Q8umnn65atcqIp1+4cGFDUwQDgHUpKSkpLS3Vt1ZZ\nWRmj060uqqurHR0d9b33JpVK+/fvj8QOAGyYfr8WV65cmZeX99133xnl3JMmTVq7dq1RmgIA\ns6usrHzw4IG+tSoqKgyYBxsAADTS70EGRVHffvttdHQ0+xNPmTLlu+++s9jpeQEAAACsjt55\nFY/H279//+HDhxnLgunO3d39+++/P3ToEMt1yQAAAABAlYE3zCZNmnT//v1169YFBQXpXqtV\nq1arV6/Oysp67bXXDDsvAAAAADREv8ET6mQy2YULFy5fvnz16tXExMTS0lLVBimK8vLyCgsL\ne+655/r16zdkyBAul8s6ZgCwRFlZWRkZGfrWMqyPHQZPAABoxDaxY5DL5RUVFeXl5TRNe3l5\neXh4oBcdQDNh+YmdWCwOCAhwc3PT93QtWrTACA8AsApGnqiTw+F4eXl5eXkZt1kAAPbkcnl+\nfr6jo6NetWiatre3R2IHAFYBt9MAAAAAbASW1gEAaIRUKhWJRPrW4vP56IsCAE0MiR0AQCOu\nX7+ekpKib60uXbqEhISYIh4AgIYgsQMA0IamaS6Xq2/PPEIIJgEAgKaHxA4AwCSuXbuWlJSk\nb61u3brhPh8AGAyJHQAw1dfXSyQSfWuJxWJTBGO9uFyus7OzAbVMEQwANBNI7ACAKS4uzoAV\n/2pqagxeaRBYqq2tra+v17cWj8ez8GlcaJrOzc01YL5VX19fV1dXU4QEYOGQ2AEAE0VRfD7f\ngFqmCAZ0kZeXZ8Ds0EKh8JVXXjFFPMYikUhu3bplb2+vb8VOnTohsYPmCYkdAIDV4/P5BuTi\nBjxwJ4Tk5OTcvHlT37tocrl89OjR7u7u+p6OoijMGgOgOyR2AADNlEgk+vnnn/WtVV9f7+np\nqe8NWpFIlJ2drW9iJxaLpVKpXlUUrl+/fvv2bX1r9ejRo0OHDgacDsByILEDALAgCQkJBmQk\nQqHQgA6OFEUZMI2LYff55HL5gwcPHBwc9Kolk8nkcrkBp+NwOAa8NeMung5gFkjsAAAsiGHJ\nlgELYwCATULHBQAAAAAbgcQOAAAAwEbgUSwAAAAhhCQkJBiwWAhFUZMmTTJFPAAGQGIHAABA\nCCEURRmwWEhdXZ0pggEwDB7FAgAAANgIJHYAAAAANgKPYgFsmUQiMWwaMAAAsEZI7ABs2S+/\n/GJnZ6dvLZFI5OTkZIp4AADApJDYAdgyLpdrwALqQqHQFMEAAICpIbEDAAAwnFQqzc7O1reW\nXC7HurRgCkjsAAAADCeXy+/du6dvrbq6OiR2YAoYFQsAAABgI3DHDgAAoKnJZDIDHuBSFNW2\nbVtTxAM2A4kdAABAU5PJZIY9wEVipyo3N9eAGZ1kMlnHjh1NEY8lQGIHAAAAGjx9+tSAWjKZ\njMvl6lvL2dnZ0dFR31oSiSQjI0PfWnV1dUjsAAAAoBmpr68/e/asARNhVldXu7q66lsrJCSk\nU6dO+tYyjFgsPnz4sL61ZDLZq6++aop4jAuJHYB1yMjIMOyJgymCAYDmgMfj8fl8fWtxuVwD\navF4TZqQODs761ulrq7OFJEYHRI7AOuQkpJiwGoQWE8MAKBZQWIHAABgHWQyWXp6ur61aJru\n2LGjAXfRmtL169eTkpL0rSWVSt3d3U0Rj/VCYgcAAGAdZDLZgwcP9K1VV1eXkpKi77NORUcO\nA9YkNAyHwzHg8WhFRYUpgrFqSOwAAABsGU3TdnZ2+qZoUqkUy0ZbI6w8AQAAAGAjkNgBAAAA\n2AgkdgAAAAA2AokdAAAAgI3A4AmAJlVTU3P8+HEDpuKUSCSmiAcAAGwJEjuAJiWXy+3t7Q1Y\npUcsFpsiHgAAsCV4FAsAAABgI5DYAQAAANgIJHYAAAAANgKJHQAAAICNwOAJAAPJZLLa2lp9\na9XU1NA0bYp4AAAAkNgBGKioqCghIYHD0e+2t0QioWm6ydbVBgCAZgWJHYDh+Hy+vomdXC6X\nSqUmigcAAExEIpEcPnxY31pyufyll15ycHAwRUgaIbEDAAAAaARN087OzvrWEolEMpnMFPE0\nBIkdAJFIJPX19frWqqurM0UwAAAABkNiB0BycnLS0tL0rVVXV+fs7Kzvo1gAAADTQWIHNiU3\nN/fmzZv61hIKhZ6envrW4nK5+lYBAAAwKSR2YFMkEomjo6O+tUQikSmCAQAAaGJI7MBCCYVC\nA5a9N6AKAACAzUBiBxbqt99+4/P5+taqra318vIyRTwAAACWD4kdmFxWVpYB3dFkMpmLi4u+\ntQwY3AoAAGAzkNiBHsRisQHLYSUnJzs5OZkiHgAAAFCFxA708PPPP9vZ2elbSyQSIbEDAABo\nAkjsrJtYLH7y5Im+tUQiUUJCAo9nyNU3YMwpBjQAAAA0DSR2xldSUlJaWqpvrfLy8ocPH+pb\nSzG7h76DDKRSKUVRSNEAAABsDGVAlykghAiFwj///FPjAnBisdiABEgmkxkwwkAulxNC9F38\ngKZpmqYNWDLBsCBttZZcLqcoiqIofWsR/S8ZsYYPpIk/fIJvvvlqcTicpvnm0zQtl8st/wOx\n8Fr4eTFjLcWHbwAnJyctD9YiIyN9fX017kJiZ6CUlJQRI0ZoTOBat27t7u6ub4M+Pj5Pnz7V\nt5abm5sB873Z29vb29tXVVXpezrDgvT29jbgFqZhtby8vMrKyhiFLi4ufD6/srKyoR8ww87l\n4eFRW1srkUj0quXo6MjlcmtqavQ9XVN++Bo/RhOdq6FaHA7H3d1dIpFo/Ky8vLwqKyv1XVrb\n2dmZoih9P3wOh+Pp6WnAW7P8S2bwhS4vL2/op8nR0dHBwaGmpobxo+Hi4iKXy/VdYZnL5bq5\nuZWXlxsQpJV+87Xz9fU1oPuNm5ubWCwWCoVajvHw8JDL5ap/F/h8vpOTU2Vlpb6ns/xvflNe\nMnd397q6On3/UnA4nJycnIb+THM4nO3bt0+ePFnjXiR2YPuWLFly+vTp48ePt2jRwtyxgK7K\nysqGDx8+cODADRs2mDsW0MOuXbv27Nmzffv2Pn36mDsW0MOQIUM8PT2PHj1q7kCALaxfDgAA\nAGAjkNgBAAAA2AiMigXb5+npGRAQYECnVzAjLpcbEBDg7e1t7kBAP25ubgEBAfb29uYOBPTT\nokULA3qHgwVCHzsAAAAAG4FHsQAAAAA2AokdAAAAgI1AYgcAAABgIzB4AmxWYWHhnDlz5HL5\nzJkzX3jhBY0HHD9+/Pbt20+fPnVwcBAIBAMHDhw5cqSdnV3TRwtKuC7WAj9i1uXJkydHjhzJ\nyckpKChwcnJq1apV165dx40bp74oJS6cVUNiB7aprq5ux44dWtZySUpKWrt2rXIqdrFYXFVV\nlZmZeeHChZUrVzo7OzdVpPAfuC7WAj9i1uX8+fM7duxQXo66urqnT58mJyefOnUqJiamXbt2\nyiNx4awdHsWC7aBpurq6Oi8v79dff/3ggw9SU1MbOrKiouLrr79W/uby9PR0cHBQbGdlZf3v\nf/9rinBBDa6LhcOPmJV68uTJ9u3blZfDxcVFee+tuLj4q6++Uq5LiQtnA3DHDmzHjRs3Vq9e\nrcuRJ06cqK6uJoRwOJylS5eGh4fTNL158+b4+HhCyIULF15//XU/Pz/ThgtqcF0sHH7ErNSh\nQ4dEIhEhhMfjxcTE9O7dWyaTHTp06MiRI4SQwsLCP//8c/z48QQXzibgjh00R9euXVNshIWF\nhYeHE0Ioipo6daqikKbphIQEc8XWnOG62AxcSouSnZ2t2BgyZEjv3r0JIVwu97XXXlMun52V\nlaXYwIWzAbhjB7ajY8eOMTExim2hULhp0yaNh4nF4ry8PMV2aGiostzDw6N169b5+fmEkMzM\nTBMHC0y4LpYPP2LWiKbpgoICxXbHjh2V5RRFtWnTpqioiBCiOAAXzjYgsQPb4eXlFRERodiu\nq6tr6LCqqirlgiv+/v6qu/z8/BS/vCoqKkwWJmiG62L58CNmjWiaHjdunGI7ODhYdVdtba1i\nQzEkAhfONiCxg2anpqZGue3o6Ki6S/lS+fsOmgyui83ApbQoHA7nzTffVC8vKSlJT09XbD/z\nzDMEF85WoI8dNDuqv7wYS5Urf3mpHgNNA9fFZuBSWr6ampovv/xSIpEQQhwcHMaOHUtw4WwF\n7tiBlXn8+LHyv0ylDh06BAYG6tiC8lmDOoqiFBtSqdSw8MBguC42A5fSwhUVFX3xxReK7nRc\nLnfx4sU+Pj4EF85WILEDK5Oenr5hwwZG4cyZM3VP7FxcXJTb9fX1qruULxmPIaAJ4LrYDFxK\nS3bp0qVt27YpLoSLi8tHH33Us2dPxS5cONuAxA6aHVdXV+W2ch5OBeUvL9VfcNA0cF1sBi6l\nZZJKpbt37z5x4oTiZVBQ0CeffCIQCJQH4MLZBiR20Oy4ublRFKV46FBcXKy669GjR4qNVq1a\nmSGy5g3XxWbgUlqg6urqFStWKCcriYyMnDNnDqMjHS6cbUBiB1YmMjIyMjKSTQt2dnZBQUG5\nubmEkNu3b7/88suK8rKyssLCQsV2hw4dWEUJ+sN1sRm4lJZGJpOtXbtWmdXNnDnzhRdeUD8M\nF842YFQsNEfKubju3Lnzxx9/CIXCkpKSjRs3Kgo5HM5zzz1nvuiaL1wXm4FLaVFOnjypXNi3\nX79+ERERpf9VWVmp2IsLZwMoLaNgAKxXXV3dlClTFNvq/55WVlbOnj1bsSSiujFjxsyaNcvk\nIYIaXBcrgh8xK/L+++8rl5TQqEOHDopBabhwNgB37KA5cnd3X7BggYODg/qukJAQjZN5QhPA\ndbEZuJSWo66uTntWpwoXzgagjx00Uz179ty4cWNcXFxycnJZWZmbm1tQUFD37t1Hjx7N5/PN\nHV3zhetiM3ApLYRiNVjd4cJZOzyKBQAAALAReBQLAAAAYCOQ2AEAAADYCCR2AAAAADYCiR0A\nAACAjUBiBwAAAGAjkNgBAAAA2AgkdtAs5OXlUf+lcanEZgKfBoClsaKfypKSkgMHDkyZMqVX\nr16tW7d2cHBwd3dv165dZGTkkiVLTp06JZfLzR1js4bEDixIWloapWbRokWNVhw1ahSj1scf\nf9wEATckISGBEc+0adPMGA8oafyOlZaWNlqxbdu2jFoHDhxogoDN7sCBA4w33rZtW3MHRYhV\nZUI248GDBxMnThQIBFOnTj18+PCtW7cePnwoEomqqqpycnIuXLiwZs2akSNHdujQYdOmTVKp\n1NzxNlNI7MDSbdq0KSMjw9xRAAA0a5999lnnzp1/+eWXRtc1yMnJmT9/fu/eve/evds0sYEq\nJHZg6SQSybx588wdhTktXLiQcWfi1KlTLI8E24ZvAhiRVCqdOnXqihUrxGKx7rWSk5MHDhyY\nnJxsusBAIyR2YAVOnToVGxtr7igAAJqjmTNnGtbxoLy8/Pnnny8sLDR6SKAFz9wBAOhk/vz5\nI0aMcHBwMKw6n88PCQlRLQkMDDRGXFYJnwaApbHYn8qff/55//796uUURUVERAwbNiwwMFAk\nEmVnZ1+8ePHWrVuMw0pLS2fPno3/zJsSEjuwDjk5OevXr1+6dKlh1Vu2bJmenm7ckKwXPg0A\nS2OZP5Xl5eXvvPOOenmvXr327t3bpUsXRvlvv/02e/bsoqIi1cK4uLjz589HRkaaLk5QhcQO\nrMaaNWvefPPN1q1bN8G5aJo+ffr0wYMHFcO+3N3dg4ODp0+fPmXKFDs7O0LIzJkzVY8fNWpU\nVFSULi3n5eXt3bv3999/f/jwYXV1ta+vb58+fSZPnjxhwgQOx4K6Rhw/fjwuLk615IMPPggN\nDRWLxfv37//pp5/+/vvvsrIygUAQHBz82muvTZo0SfV+akpKyv79+8+cOfPo0aO6ujpfX9/w\n8PAJEyZMmTKFx2vk145QKDx+/Pjvv/+emJj4+PHj6upqPz+/gICAYcOGRUVF9ejRwyRv2KiM\n8hZu3bqlGHiYlZVVWVkplUoDAgICAwNbtWoVGho6efJkCxmdSgi5evXqvn37VEsmTpw4fPhw\nQkhqauq+ffvOnj1bWFhYU1Pj4+PTvXv3F154YerUqfb29tqblUgkcXFxP/zww507dwoLC93d\n3YOCgqKioqZNm+bn56dXhDbwpSIm+5y12LNnT1lZGaNw6NChf/zxh8Zmx48fLxAIBg0axOiN\nt2/fPiR2TYcGsBiNDqGaOHGixoojR45kHBkTE6N6wPXr1xkHTJ06taEw8vPzhw4dqjGA3r17\nP3r0iFYbFKbj6Xbv3t3Q0+S+ffsWFBRojOejjz5iHHzy5Ek2R+ryaXz22WfqTaWnp3fr1k1j\n/KGhoenp6TRNi8XiRYsWNZSkdu3a9f79+w198jRNHzlyJCgoSGNdhaioqLy8PC0taKfxO/b0\n6dNGK7Zp04ZRa//+/SZ6CxkZGQMGDNDSAiFEMYdOaWmpxhZ0/840Sv0xXJs2bRo9Zv369VKp\n9NNPP23omxAUFJSYmKjlvElJSV27dtVY18fH588//8zNzWWUjx07VmNThl0RkUgUGhrKONLe\n3l79yIyMDC6XyziyV69eUqlU989Zl59KU3zOWkilUvXPTSAQVFZWaq/4xRdfMGo5OztLJBLD\nwgB9IbEDC6LL2Pj4+Hj1ikZM7O7fvy8QCLQE0LZtW/Vpz3Q53fr167W/tc6dO9fU1Cgb2bZt\nm/bjlefV/UjdPw31xG7jxo3+/v5aThEQEPDkyZNXX31VeyTt2rUrKyvT+OF/+umnjb4RQoi3\nt3dSUpK2b1LDTJ3YsX8LiYmJbm5uujRCCOnWrZvqh6nvN0EXhiV2X3311YwZM7RH4urqmpmZ\nqfGk8fHx2jvU8vn8gwcPMgo1JnZsrsi1a9fUE6Zp06YxDps8ebJ6eCkpKXp9zoYldiw/Z+3O\nnj2r3tq3337baMWioiL1TFfxjx80AQt69AOgEaND8fvvv2+6eS+rqqqGDx9eXFys5ZicnJzX\nXntN35avXbu2ePFi7cfcu3dv1apV+rbcZD766KPHjx9rOaCwsLBLly4//vij9nays7PXrFmj\nXr5u3brPP/9cl0hKS0uHDh2alZWly8FNif1bqK2tHTduXFVVlY5nTElJiYmJ0S/KJrFv3769\ne/dqP6a6uvrtt99WL7937964ceOEQqGWuhKJZNasWY2GwfKK9O3b97333mMcefDgwb///lv5\nMiUl5ciRI4xjFi9e3NDtRuNi8zk36sqVK4wSd3f3V155pdGKAoFg1KhRDv+l+qGBSSGxA0u3\nceNG1X+a09LStm/fbqJzLVu2LDs7W73cycnJ2dlZ+fLkyZP6tpyRkSGTyZQvKYrSeNiOHTtE\nIpG+jTcNXeJn5MQNHbZnzx7GokNpaWnLly9nHNa6des333xz0aJFw4YNY9wAaKhPtxkZ5S1s\n3LhRfW4IDw+PHj16REZGhoSEqH+ke/fuLS8vN8Y7MKZ79+6pvmzom3D+/Pnbt2+rltA0/fbb\nb9fU1Kgf7OLi4uTkpHxZX1+vPQajXJHVq1cz+vXKZDLVUVxLly6l/9s3Izg42OBhXvoy+HPW\nxdWrVxklL7zwgo5TExw/frz+v8aPH69vAGAYJHZg6Xr16jV9+nTVkuXLlz958sToJ8rJyVF/\nktW5c+eLFy/W1tbW1NRcuXIlLCyMzSlCQkJiY2OLi4tramrOnz+v/j99VVXVjRs3FNvOzs6B\ngYGBgYEuLi6Mw3x8fBS7FM/sdD+SJUdHx02bNmVnZ4tEoqSkpGHDhhl2WHl5eVpammrJ0qVL\nGRltVFTUvXv3Dhw4sG7dujNnzvz111+M/vLx8fHHjh1j/6YIIT4+PurrjDGo9+hiMMpb+OGH\nHxjNrl69uqioKCkp6dy5c+np6WlpaYyHwjKZ7Ny5c4rtJvsm6Kh///6nT58uKSmpra29efOm\nops/w5kzZ1Rf/vLLL+o3igYMGHDr1q3q6ura2trLly/37NlTl7Mb5Yq4uLjs2rWL0fLRo0cV\n83pcu3bt999/V91FUdSePXvYjFcwgAGfsy7U5y7p06ePgSFCUzL3s2CAfzXU/6mkpMTDw0O1\ncMaMGaoVjdLHTr0vTtu2bRm9werr6/v27aseZKOnI4QMGDCgvr5e9bAnT56o/5Xdvn07IzAL\nGTxBCDl79qzqMbW1tRr7pOtymGpgjx49YgyVDQwMFAqFjJCOHj3KaGTEiBEaPwctjLjGkWof\nO6O8hYKCAl3eoPpUsZs2bWIcY/bBE4SQt956Sy6Xqx4ml8vDw8MZh7322muqx6iPW4qMjGT0\nu6+tre3evbv6GVX72Bn3S6Xe++L555+naXrw4MGM8nfffVf/z5imDe1jZ/Dn3CiZTKZ+/+/8\n+fOGvTtoSrhjB1bA19d3xYoVqiXffvvtzZs3jXuW77//nlHy+eefe3p6qpY4ODjs2bPHgMZ5\nPJ76kFgfH5+pU6cyjnz69KkB7TeB4cOHDxkyRLXEyclpxIgRhh2m+qztl19+YfSbXLx4sfo9\nj6ioKMYdU8V0Kvq8CVMxyltQ73I3ZswY9XOpz/RRUVFhQMwmJRAINmzYwMgMKIr68MMPGUeq\nDkUqKiqKj49nHLBt2zZGiubk5PT1119rD8C4X6pNmzb5+PgwDvvkk0+U90oVAgMD165dqz0w\n4zLsc9aFYugro1DfWWbALJDYgXWYPXv2s88+q3xJ0/T777+v/nvHYEVFRTk5Oaolnp6eEyZM\nUD8yNDS0oclQtAgJCQkODlYvVy9stOeQuWicgKNFixaGHaYqISGBUaKeCCownurK5XL1bkBm\nYZS30KNHj9v/FR0drd6C+pNKCxQZGenq6qpe3qlTJ0aJ6iCJq1evMn6oIyMj1eccIYQMGTJE\n4w+UknG/VD4+Phs3bmQUqk/qsWPHjqZ80k0M/Zx1oT59HSGkid8dGAaJHVgHHo+3ZcsW1ZKE\nhASNDyYMo+zZpvTSSy811E34rbfe0rf9zp07ayy3kFWDdKFxOlz1hzU6HqaK0ZXHzc2tQ4cO\nGo9Un0g2MTFRS8tNxihvwd3dvdt/qf4dLSsr++uvvxYvXqxxTLGlaegLr33GHPVsTMsIdO0T\n6xj9S/X666+rd/lQNXny5BdeeEHLAaZg2OesC43/NlvUJOrQEKw8AVZj8ODBEydO/Pnnn5Ul\nMTExUVFR7u7u7Bt/8OABo6R9+/YNHcyYgUUXqqP5VKnP9mSxdBwNZ8B6voy+ZfX19e3atdN4\npPpdB8YgDMOcPHmS0YlT3UsvvcRYKEmVKd6C4tbR5cuXExMTExMTGXeULVxDX3jtmUFeXh6j\npKGPUfsuYporsmvXrtDQ0NraWvVdXl5ejP88m4Zhn7MuvLy81Aurq6u1330HS4DEDqzJ119/\n/ccff9TV1SlelpSUrFixYsOGDexbVu+lFBAQ0NDB2mexB71IJBLGX0qJRNLoEFQlo8z0ER4e\n7u3trf0YLeMcjf4Wbt++vXPnztjYWO0TB9oe9Y+iVatWDR2s5W63ib5UQUFBq1ev/uCDD9R3\nbdiwwcb6n3l4eFAUxbhvZ4ET64A63FYFa9KqVSvGdKxbt25lzORkGPXETsv6E56eng39owz6\n0n0yXo0qKyuNFYnBjPsWli9fHhYW9s033zS3rI5o+iS1ZEtafkJN96WaMWOGo6Mjo9Db27vR\nBVesDofDUb9pZ5RftmBqSOzAyixcuFC1F5dUKp07dy77URR8Pp9RouVvg1gsttghDlaH5Yxf\nlnAhjPgWvvjii5UrVzJmb1Zyd3d/4403Vq5cyeZ0lkx1GnAFLWM5tdw9Mt2XauXKlep7S0tL\nG10w0BqpzxeYnJysY12pVCr6L9OtGAQMSOzAyjg4ODCGp509e5b90Ej1jnrqk4op5efnG3FA\nbjPn4uLC6GjYvXt33WdssoSFxYz1FgoLC9WnD3R2do6Kitq5c2dycvLTp08PHjw4aNCgpnlf\nTU+9p+PDhw8bOljLLhN9qW7dutVQx48VK1ZkZGQ0+MasU0REBKMkLi5Ox199EydOZCwptnnz\nZhPECBogsQPr8+KLLzJmLqiurmbZpnoXq/z8/IYOtq5u7JaP8eFb48drlLdw+PBhiUSiWtK/\nf//79+8fPXr0nXfe6d69u2I6Ny3fTGun3m1Oyyepvc+c0b9UEolkxowZqgvrqRKJRIpZ01me\nxaL069ePUZKXl8eYuk8juVx+4cIFRmFDo5LB6JDYgVXavHmz+sNTNrp06cIo0fK/6aFDh4x4\naujWrZvqy8rKSguccVc7o7wF9Vs+20T3kdEAAAf9SURBVLZtU+9JpvsgAKujvmTf4cOHGzr4\nyJEjWpoy+pdq3bp1d+7cUS1hjDy9cuXKjh072JzC0gwePFg91V64cGFDXQWUrl27pv6gXON8\nhGAKSOzAKgUHB8+bN8+IDfbs2ZPxazo7O/v06dPqRxYVFakv6NnEGrptwOZIM1JfgFL9330F\nuVxe+V+W0MeOGOktZGdnMw7u2LEjo4SmadUZf3RnFd8E9fX6Tp06lZmZqX7k1atXtU9haNwv\nVXp6+qpVq1RLuFzuoUOHGL80YmJibOl+Ko/He+eddxiFSUlJCxYs0FJLJBKpr3vRtWtX3LFr\nMkjswFotW7ZMy7A4fbm4uKgvmfDJJ58w5riSy+XvvfeeWCw21nkNo/uyYxa7QJkq9YWzGlqX\n6X//+5/Hf+3bt8/0ATbOKG+hpKSEcbB6qnfs2DHGfSMdWcU34ZlnnmFMF0zT9Lx58xi3iEQi\nkfbcghj1SyWXy2fMmCESiVQLo6OjJ02a9Prrr6sW1tTUzJo1S3tg1mXmzJnqq01s2rRpxowZ\nGscOp6amjhw5Un2+dy0TTYPRIbEDa+Xq6rpu3TojNjht2jRGSWJi4rBhw1JTUxUvk5OTR4wY\ncezYMSOeVBfq/f+2bdt248aNzMxMxggP3Y+0KH379mWs6X79+vWPP/6YMYwuPj5+6dKlqiVO\nTk6TJ09uihAbY5S3oH755s2bpxwWKpPJdu/ereMfSCv9JhBNP4YnT54cOXKkctLgmzdvRkZG\nXr9+XXs7RvxSbd++/dq1a6olDg4OitWrV6xYYWdnx4hWfdVp6+Xn57dt2zb18m+//bZt27Zv\nv/32/v37T548+dNPP61ZsyYqKqpbt27nz59nHBwUFPTee+81RbhACMEExWDV3njjjV27djF+\n5xps8uTJK1euZNwjuXLlSteuXb28vKRSKcvJsQymvibmzZs3FU+aYmJiVBeY0v1IS/PZZ5+N\nHz9etWTt2rXHjx/v379/q1atysrKLl++rH4bYMmSJY1OLNxk2L+FsLAwRs/0+Pj4oKCgLl26\nUBSVkpKinJq7Udb7TXj77be3bNly//591cIzZ848++yz+v4YGuVLlZeXt2TJEsYxc+fOVXQ+\na9OmzaxZs7Zu3aq694MPPhg+fLjNzFf8xhtv/PHHH+qdHcvLy3fv3r17927t1Tkczo4dOzDx\nZ1NCYgdWjKKorVu39u7du9HOvLpwcHDYuXPnyJEj1cdMaFwPu8lERER4eHjo0vVb9yMtzYsv\nvhgdHX3gwAHVwrS0NC0rhg0ePHjhwoWmD01X7N/Cq6+++vXXXzO+frW1/9fe3YQks4VxAB9f\nSsEUG6kkzI9FtGgjUhRUi0CwUClo6SIyKUgw+pAgsBYJEkSbQKiIqCjQhCQShhYRRBDMIlpk\nkLvahNZEmJegoN5Fca/MvJWmb43e/2/n8KhncpTTec48zz+s1SmhUMjaDMDdqJ6/V4JIJJqf\nn29ra+NuCsz0a5iTi6q/vz+ZTKYeIUkytUy62+1eXl5ObXTBMMzg4KDf789otHy2trb2/Pz8\nhc2dAoFgaWnJZDL9jVHBe5CKhfxWV1dnt9tz9WpGozGdYkuvWZhvo1AofD5fOs0f04/koYWF\nha6urjSDjUZjKBRiZcF+XJanoNfr+/r6Pn6WRqPZ2dlhHeQuWuf1lWAwGD5dByIIwul0fhqT\n5SeyurrKvYNqfHycJMl/H1ZUVAwPD7NiAoHA9vZ2mu/Lf0Kh0O/3j4yMZNTbWqFQhEIhbm4d\n/ra8/NoDpPJ6vam/s1lyOp1+v/+9NApJkuvr65OTk7l6uzRZrdZIJNLT06PT6eRyeVFRkUwm\nUyqV3ERk+pF8IxKJgsHg9PQ0t1h0qvLy8rm5OYqiPg77Edmfgs/n++AfFavVStN0a2urVCpN\nPU7TtNfr5Qbn6ZVAEITNZqMo6r1+zWKx2OfzfXr/BJHdJxKLxbh3d1ZVVXEnlC6Xi9t9y+Fw\n8KHfXa78+vVrdnb25OSkvb3902C5XD46OhqJRDo7O79hbMDCbvELAARB3N3dbWxsbG1tRaPR\neDwukUi0Wm1HR8fAwMDrnE8gEKTG83zfUn65vb0NBoMURUUikXg8/vT0pFarNRqNVqs1Go0m\nk4nbrJNvsjyFw8PDlZWVs7OzaDT6+PioUqkMBkN3d3d9ff1rwOLiImuVTqlUsupxFIBkMhkM\nBgOBwPn5+dXVlVQqVavVFoult7dXo9EwDONyuVLjdTrd0NDQH1+qAC4q/ri8vAyHw7u7uxcX\nF7FYjGEYsVhMkqRSqWxsbGxqasLf82dhYgeQsYeHB9ZeYLfb7fF4fmo8AAAAr5CKBcgYt5BY\nDivqAQAAfBnuigUgCIK4v793OBypR17v0i8pKeEGc3sZcatLAAAAfD+kYgHeyGQyVomsiYmJ\nqakpVhhFURaLJbXASmlpaTwez23vWgAAgC9AKhbgDas7EEEQHo/HZrPRNJ1IJBiGOTg4sNvt\nZrOZVTZvbGwMszoAAOADrNgBvLm5udHr9Zk2XGppadnb2+NbQTUAAPh/woodwJuysrL9/f3a\n2tr0n2I2mymKwqwOAAB4AhM7gP9UV1cfHx/PzMyoVKqPIxsaGjY3N8PhsEQi+Z6xAQAAfAqp\nWIA/eHl5oWn66Ojo9PT0+vo6kUgUFxfLZLLKykq9Xt/c3FxTU/PTYwQAAGDDxA4AAACgQCAV\nCwAAAFAgMLEDAAAAKBCY2AEAAAAUiN9pVg6ztwIYCgAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#binned heat.index plots\n",
    "dt <- read.csv(file=\"model_data/SIFig2B_heatindex_sleep_model_output.csv\",header=T)\n",
    "flex.plot <- binned.plot(felm.est = flex_hi,\n",
    "                 plotvar = \"cutheat.index\",\n",
    "                 breaks = 5,\n",
    "                 omit = c(5,10), \n",
    "                 linecolor = \"purple\",\n",
    "                 pointfill = \"purple\",\n",
    "                 errorfill = \"purple\",\n",
    "                 xlabel = \"Nighttime Heat Index in C\",\n",
    "                 ylabel = \"Change in Sleep Duration (Minutes)\"\n",
    "                 )\n",
    "# hist <- axis_canvas(flex.plot, axis = \"x\") + \n",
    "#         geom_histogram(data = Climate_Controls_DF, aes(x = Climate_Controls_DF$heat.index), bins=41, fill='gray60', color='gray60', alpha=0.75, size=0.1)\n",
    "# flex.plot <- insert_xaxis_grob(flex.plot, hist, position = \"bottom\", height = grid::unit(0.1, \"null\"))\n",
    "flex.plot <- ggdraw(flex.plot)\n",
    "# ggsave(\"flexplot_hi.pdf\")\n",
    "flex.plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{S6. Flexible Nighttime Heat Index Regression} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{1}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-2} \n",
      " & Flexible Heat.Index \\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " cutheat.index(-Inf,-15] & 2.485$^{***}$ \\\\ \n",
      "  & (0.764) \\\\ \n",
      "  cutheat.index(-15,-10] & 1.140$^{*}$ \\\\ \n",
      "  & (0.654) \\\\ \n",
      "  cutheat.index(-10,-5] & 0.616 \\\\ \n",
      "  & (0.569) \\\\ \n",
      "  cutheat.index(-5,0] & 0.206 \\\\ \n",
      "  & (0.436) \\\\ \n",
      "  cutheat.index(0,5] & 0.598$^{**}$ \\\\ \n",
      "  & (0.240) \\\\ \n",
      "  cutheat.index(10,15] & $-$2.197$^{***}$ \\\\ \n",
      "  & (0.205) \\\\ \n",
      "  cutheat.index(15,20] & $-$4.435$^{***}$ \\\\ \n",
      "  & (0.360) \\\\ \n",
      "  cutheat.index(20,25] & $-$6.513$^{***}$ \\\\ \n",
      "  & (0.481) \\\\ \n",
      "  cutheat.index(25, INF] & $-$7.069$^{***}$ \\\\ \n",
      "  & (0.688) \\\\ \n",
      "  cutheat.index.NORM & $-$0.219$^{***}$ \\\\ \n",
      "  & (0.077) \\\\ \n",
      "  cutprcp(0,1] & 0.274$^{***}$ \\\\ \n",
      "  & (0.105) \\\\ \n",
      "  cutprcp(1,2] & 1.216$^{***}$ \\\\ \n",
      "  & (0.283) \\\\ \n",
      "  cutprcp(2,3] & 1.296$^{***}$ \\\\ \n",
      "  & (0.350) \\\\ \n",
      "  cutprcp(3, Inf] & 2.099$^{***}$ \\\\ \n",
      "  & (0.535) \\\\ \n",
      "  PRCP.NORM & 2.172$^{**}$ \\\\ \n",
      "  & (1.098) \\\\ \n",
      "  cuttrange(-Inf,0] & 2.702$^{**}$ \\\\ \n",
      "  & (1.204) \\\\ \n",
      "  cuttrange(0,5] & 0.223 \\\\ \n",
      "  & (0.176) \\\\ \n",
      "  cuttrange(10,15] & $-$0.548$^{***}$ \\\\ \n",
      "  & (0.125) \\\\ \n",
      "  cuttrange(15,20] & $-$2.037$^{***}$ \\\\ \n",
      "  & (0.277) \\\\ \n",
      "  cuttrange(20,Inf] & $-$2.295$^{***}$ \\\\ \n",
      "  & (0.719) \\\\ \n",
      "  TRANGE.NORM & $-$0.030 \\\\ \n",
      "  & (0.146) \\\\ \n",
      "  cutcloud_rean2(0,20] & 0.498 \\\\ \n",
      "  & (0.438) \\\\ \n",
      "  cutcloud_rean2(20,40] & 0.344 \\\\ \n",
      "  & (0.468) \\\\ \n",
      "  cutcloud_rean2(40,60] & 0.600 \\\\ \n",
      "  & (0.456) \\\\ \n",
      "  cutcloud_rean2(60,80] & 0.933$^{*}$ \\\\ \n",
      "  & (0.485) \\\\ \n",
      "  cutcloud_rean2(80,100] & 1.540$^{***}$ \\\\ \n",
      "  & (0.459) \\\\ \n",
      "  CLOUD.NORM & $-$0.028 \\\\ \n",
      "  & (0.023) \\\\ \n",
      "  HUMID.NORM & 0.108$^{***}$ \\\\ \n",
      "  & (0.041) \\\\ \n",
      "  cutwind_rean2(5,10] & 0.481$^{***}$ \\\\ \n",
      "  & (0.117) \\\\ \n",
      "  cutwind_rean2(10,Inf] & 0.785$^{***}$ \\\\ \n",
      "  & (0.229) \\\\ \n",
      "  WIND.NORM & $-$0.336$^{*}$ \\\\ \n",
      "  & (0.195) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes \\\\ \n",
      "Date FE & Yes \\\\ \n",
      "State:Month FE & Yes \\\\ \n",
      "Observations & 4,405,171 \\\\ \n",
      "R$^{2}$ & 0.284 \\\\ \n",
      "Adjusted R$^{2}$ & 0.274 \\\\ \n",
      "Residual Std. Error & 77.707 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{1}{r}{Standard errors are in parentheses and are clustered on the state (admin 1) level} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# invisible(stargazer(flex_hi,\n",
    "#           type='latex',\n",
    "#           title = \"S6. Flexible Nighttime Heat Index Regression\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Flexible Heat.Index'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           covariate.labels = c(\"cutheat.index(-Inf,-15]\", \"cutheat.index(-15,-10]\", \"cutheat.index(-10,-5]\", \"cutheat.index(-5,0]\", \"cutheat.index(0,5]\", \"cutheat.index(10,15]\", \"cutheat.index(15,20]\", \"cutheat.index(20,25]\", \"cutheat.index(25, INF]\", \"cutheat.index.NORM\", \"cutprcp(0,1]\", \"cutprcp(1,2]\", \"cutprcp(2,3]\", \"cutprcp(3, Inf]\", \"PRCP.NORM\", \"cuttrange(-Inf,0]\", \"cuttrange(0,5]\", \"cuttrange(10,15]\", \"cuttrange(15,20]\", \"cuttrange(20,Inf]\", \"TRANGE.NORM\", \"cutcloud_rean2(0,20]\", \"cutcloud_rean2(20,40]\", \"cutcloud_rean2(40,60]\", \"cutcloud_rean2(60,80]\", \"cutcloud_rean2(80,100]\", \"CLOUD.NORM\", \"HUMID.NORM\", \"cutwind_rean2(5,10]\", \"cutwind_rean2(10,Inf]\", \"WIND.NORM\"),\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\",\"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\",\"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the state (admin 1) level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Supplementary Figure 3: Nighttime temperature anomalies and sleep duration\n",
    "#Binned Nighttime Minimum Temperature Anomalies and Sleep Duration FE Regressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ cuttanomaly + CUTPRCP +      prcp_norm_w7 + CUTTRANGE + trange_norm_w7 + CUTCLOUD_rean2 +      cloud_norm_7_rean2 + CUTRHUM_rean2 + rhum_norm_7_rean2 +      CUTWIND_rean2 + wind_norm_7_rean2 | userID + unique_day_no +      stateyrmn | 0 | state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-383.99  -49.12   -2.32   45.76  408.50 \n",
       "\n",
       "Coefficients:\n",
       "                         Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "cuttanomaly(-Inf,-10.5]  1.418270     0.935054   1.517 0.129323    \n",
       "cuttanomaly(-10.5,-9.5]  1.494103     1.097824   1.361 0.173524    \n",
       "cuttanomaly(-9.5,-8.5]   0.550626     0.875648   0.629 0.529467    \n",
       "cuttanomaly(-8.5,-7.5]   0.993816     0.688216   1.444 0.148726    \n",
       "cuttanomaly(-7.5,-6.5]   0.481076     0.517336   0.930 0.352417    \n",
       "cuttanomaly(-6.5,-5.5]   0.163304     0.384638   0.425 0.671155    \n",
       "cuttanomaly(-5.5,-4.5]  -0.054596     0.438523  -0.124 0.900920    \n",
       "cuttanomaly(-4.5,-3.5]   0.069831     0.255781   0.273 0.784845    \n",
       "cuttanomaly(-3.5,-2.5]   0.170992     0.247915   0.690 0.490371    \n",
       "cuttanomaly(-2.5,-1.5]   0.166225     0.235260   0.707 0.479840    \n",
       "cuttanomaly(-1.5,-0.5]   0.196856     0.185340   1.062 0.288174    \n",
       "cuttanomaly(0.5,1.5]    -0.205279     0.157437  -1.304 0.192275    \n",
       "cuttanomaly(1.5,2.5]    -1.159016     0.167764  -6.909 4.89e-12 ***\n",
       "cuttanomaly(2.5,3.5]    -1.480546     0.188643  -7.848 4.21e-15 ***\n",
       "cuttanomaly(3.5,4.5]    -1.686270     0.257287  -6.554 5.60e-11 ***\n",
       "cuttanomaly(4.5,5.5]    -2.117635     0.260581  -8.127 4.42e-16 ***\n",
       "cuttanomaly(5.5,6.5]    -2.095399     0.309364  -6.773 1.26e-11 ***\n",
       "cuttanomaly(6.5,7.5]    -3.045778     0.391262  -7.784 7.00e-15 ***\n",
       "cuttanomaly(7.5,8.5]    -2.998676     0.476220  -6.297 3.04e-10 ***\n",
       "cuttanomaly(8.5,9.5]    -3.025598     0.573983  -5.271 1.36e-07 ***\n",
       "cuttanomaly(9.5,10.5]   -3.522202     0.988126  -3.565 0.000365 ***\n",
       "cuttanomaly(10.5, Inf]  -3.969815     0.761436  -5.214 1.85e-07 ***\n",
       "CUTPRCP(0,1]             0.335274     0.104530   3.207 0.001339 ** \n",
       "CUTPRCP(1,2]             1.221733     0.283591   4.308 1.65e-05 ***\n",
       "CUTPRCP(2,3]             1.347377     0.345648   3.898 9.69e-05 ***\n",
       "CUTPRCP(3, Inf]          1.979464     0.533047   3.713 0.000204 ***\n",
       "prcp_norm_w7             2.534060     1.120759   2.261 0.023758 *  \n",
       "CUTTRANGE(-Inf,0]        2.998287     1.218838   2.460 0.013895 *  \n",
       "CUTTRANGE(0,5]           0.457786     0.170470   2.685 0.007243 ** \n",
       "CUTTRANGE(10,15]        -0.568240     0.117911  -4.819 1.44e-06 ***\n",
       "CUTTRANGE(15,20]        -1.978014     0.294651  -6.713 1.91e-11 ***\n",
       "CUTTRANGE(20, Inf]      -2.008097     0.732857  -2.740 0.006142 ** \n",
       "trange_norm_w7           0.244607     0.140284   1.744 0.081220 .  \n",
       "CUTCLOUD_rean2(0,20]     0.553292     0.437302   1.265 0.205786    \n",
       "CUTCLOUD_rean2(20,40]    0.474527     0.458037   1.036 0.300202    \n",
       "CUTCLOUD_rean2(40,60]    0.760232     0.451936   1.682 0.092537 .  \n",
       "CUTCLOUD_rean2(60,80]    1.148406     0.476810   2.409 0.016018 *  \n",
       "CUTCLOUD_rean2(80,100]   1.747198     0.452477   3.861 0.000113 ***\n",
       "cloud_norm_7_rean2      -0.053818     0.024276  -2.217 0.026628 *  \n",
       "CUTRHUM_rean2(0,20]     -5.086738     1.342250  -3.790 0.000151 ***\n",
       "CUTRHUM_rean2(20,40]    -1.654635     0.615438  -2.689 0.007176 ** \n",
       "CUTRHUM_rean2(40,60]    -0.004086     0.189299  -0.022 0.982781    \n",
       "CUTRHUM_rean2(80,100]   -0.338892     0.138432  -2.448 0.014362 *  \n",
       "rhum_norm_7_rean2        0.124602     0.044008   2.831 0.004635 ** \n",
       "CUTWIND_rean2(5,10]      0.595016     0.121855   4.883 1.04e-06 ***\n",
       "CUTWIND_rean2(10,Inf]    1.048034     0.235645   4.448 8.69e-06 ***\n",
       "wind_norm_7_rean2       -0.218169     0.214724  -1.016 0.309609    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.71 on 4345317 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2842   Adjusted R-squared: 0.2743 \n",
       "Multiple R-squared(proj model): 0.0001546   Adjusted R-squared: -0.01362 \n",
       "F-statistic(full model, *iid*):28.83 on 59853 and 4345317 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 15.51 on 47 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #Nighttime Minimum Temperature Anomalies and Sleep Duration FE Regressions - GHCND \n",
    "# flex_tanomaly <- felm(formula = sleep_duration_minutes ~ cuttanomaly + \n",
    "#                                           CUTPRCP + prcp_norm_w7 + \n",
    "#                                           CUTTRANGE + trange_norm_w7 + \n",
    "#                                           CUTCLOUD_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           CUTRHUM_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           CUTWIND_rean2 + wind_norm_7_rean2  \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(flex_tanomaly)\n",
    "\n",
    "# #save model coefficients for plotting\n",
    "# dt <- as.data.frame(summary(flex_tanomaly)$coefficients[, 1:4]) # extract from felm summary (use df to keep coefficient\n",
    "# write.csv(dt,file=\"model_data/SIFig4A_tempanomaly_sleep_model_output.csv\")#save data as CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 2299093 rows containing non-finite values (stat_bin).”Warning message:\n",
      "“Removed 2 rows containing missing values (geom_bar).”Saving 6.67 x 6.67 in image\n"
     ]
    },
    {
     "data": {
      "image/png": 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AbG92LUqzJrVIWxjO8oNu1ZmZUCAACA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GyFfKIjfAee78DLV2UxWZQuSeo1gq8WXd1wcsOBxw7Y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YzeJacb53+lZbkJMypbKtZeJ50buW+6JZ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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot Nighttime Minimum Temperature Anomalies and Sleep Duration FE Output - GHCND\n",
    "dt <- read.csv(file=\"model_data/SIFig4A_tempanomaly_sleep_model_output.csv\",header=T)\n",
    "flex.plot <- binned.plot(felm.est = flex_tanomaly,\n",
    "                 plotvar = \"cuttanomaly\",\n",
    "                 breaks = 1,\n",
    "                 omit = c(-0.5,0.5),\n",
    "                 linecolor = \"violet\",\n",
    "                 pointfill = \"violet\",\n",
    "                 errorfill = \"violet\",\n",
    "                 xlabel = \"Min. Temp Anomaly in C\",\n",
    "                 ylabel = \"Change in Sleep Duration (Minutes)\"\n",
    "                 )\n",
    "\n",
    "# hist <- axis_canvas(flex.plot, axis = \"x\") + \n",
    "#         geom_histogram(data = Climate_Controls_DF, aes(x = Climate_Controls_DF$tanomaly_norm_w7), bins=21, fill='gray60', color='gray60', alpha=0.75, size=0.1)\n",
    "# flex.plot <- insert_xaxis_grob(flex.plot, hist, position = \"bottom\", height = grid::unit(0.1, \"null\"))\n",
    "flex.plot <- ggdraw(flex.plot)\n",
    "#ggsave(\"flexplot_tanomaly.pdf\")\n",
    "flex.plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{S6. Main Effects: Binned nighttime minimum temperature anomalies and sleep duration regressions} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{1}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-2} \n",
      " & Flexible TMIN.Anomaly GHCND \\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " cuttanomaly(-Inf,-10.5] & 1.418 \\\\ \n",
      "  & (0.935) \\\\ \n",
      "  cuttanomaly(-10.5,-9.5] & 1.494 \\\\ \n",
      "  & (1.098) \\\\ \n",
      "  cuttanomaly(-9.5,-8.5] & 0.551 \\\\ \n",
      "  & (0.876) \\\\ \n",
      "  cuttanomaly(-8.5,-7.5] & 0.994 \\\\ \n",
      "  & (0.688) \\\\ \n",
      "  cuttanomaly(-7.5,-6.5] & 0.481 \\\\ \n",
      "  & (0.517) \\\\ \n",
      "  cuttanomaly(-6.5,-6.5] & 0.163 \\\\ \n",
      "  & (0.385) \\\\ \n",
      "  cuttanomaly(-5.5,-4.5] & $-$0.055 \\\\ \n",
      "  & (0.439) \\\\ \n",
      "  cuttanomaly(-4.5,-3.5] & 0.070 \\\\ \n",
      "  & (0.256) \\\\ \n",
      "  cuttanomaly(-3.5,-2.5] & 0.171 \\\\ \n",
      "  & (0.248) \\\\ \n",
      "  cuttanomaly(-2.5,-1.5] & 0.166 \\\\ \n",
      "  & (0.235) \\\\ \n",
      "  cuttanomaly(-1.5,-.5] & 0.197 \\\\ \n",
      "  & (0.185) \\\\ \n",
      "  cuttanomaly(.5,1.5] & $-$0.205 \\\\ \n",
      "  & (0.157) \\\\ \n",
      "  cuttanomaly(1.5,2.5] & $-$1.159$^{***}$ \\\\ \n",
      "  & (0.168) \\\\ \n",
      "  cuttanomaly(2.5,3.5] & $-$1.481$^{***}$ \\\\ \n",
      "  & (0.189) \\\\ \n",
      "  cuttanomaly(3.5,4.5] & $-$1.686$^{***}$ \\\\ \n",
      "  & (0.257) \\\\ \n",
      "  cuttanomaly(4.5,5.5] & $-$2.118$^{***}$ \\\\ \n",
      "  & (0.261) \\\\ \n",
      "  cuttanomaly(5.5,6.5] & $-$2.095$^{***}$ \\\\ \n",
      "  & (0.309) \\\\ \n",
      "  cuttanomaly(6.5,7.5] & $-$3.046$^{***}$ \\\\ \n",
      "  & (0.391) \\\\ \n",
      "  cuttanomaly(7.5,8.5] & $-$2.999$^{***}$ \\\\ \n",
      "  & (0.476) \\\\ \n",
      "  cuttanomaly(8.5,9.5] & $-$3.026$^{***}$ \\\\ \n",
      "  & (0.574) \\\\ \n",
      "  cuttanomaly(9.5,10.5] & $-$3.522$^{***}$ \\\\ \n",
      "  & (0.988) \\\\ \n",
      "  cuttanomaly(10.5,Inf] & $-$3.970$^{***}$ \\\\ \n",
      "  & (0.761) \\\\ \n",
      "  cutprcp(0,1] & 0.335$^{***}$ \\\\ \n",
      "  & (0.105) \\\\ \n",
      "  cutprcp(1,2] & 1.222$^{***}$ \\\\ \n",
      "  & (0.284) \\\\ \n",
      "  cutprcp(2,3] & 1.347$^{***}$ \\\\ \n",
      "  & (0.346) \\\\ \n",
      "  cutprcp(3, Inf] & 1.979$^{***}$ \\\\ \n",
      "  & (0.533) \\\\ \n",
      "  PRCP.NORM & 2.534$^{**}$ \\\\ \n",
      "  & (1.121) \\\\ \n",
      "  cuttrange(-Inf,0] & 2.998$^{**}$ \\\\ \n",
      "  & (1.219) \\\\ \n",
      "  cuttrange(0,5] & 0.458$^{***}$ \\\\ \n",
      "  & (0.170) \\\\ \n",
      "  cuttrange(10,15] & $-$0.568$^{***}$ \\\\ \n",
      "  & (0.118) \\\\ \n",
      "  cuttrange(15,20] & $-$1.978$^{***}$ \\\\ \n",
      "  & (0.295) \\\\ \n",
      "  cuttrange(20,Inf] & $-$2.008$^{***}$ \\\\ \n",
      "  & (0.733) \\\\ \n",
      "  TRANGE.NORM & 0.245$^{*}$ \\\\ \n",
      "  & (0.140) \\\\ \n",
      "  cutcloud_rean2(0,20] & 0.553 \\\\ \n",
      "  & (0.437) \\\\ \n",
      "  cutcloud_rean2(20,40] & 0.475 \\\\ \n",
      "  & (0.458) \\\\ \n",
      "  cutcloud_rean2(40,60] & 0.760$^{*}$ \\\\ \n",
      "  & (0.452) \\\\ \n",
      "  cutcloud_rean2(60,80] & 1.148$^{**}$ \\\\ \n",
      "  & (0.477) \\\\ \n",
      "  cutcloud_rean2(80,100] & 1.747$^{***}$ \\\\ \n",
      "  & (0.452) \\\\ \n",
      "  CLOUD.NORM & $-$0.054$^{**}$ \\\\ \n",
      "  & (0.024) \\\\ \n",
      "  cutrhum_rean2(0,20] & $-$5.087$^{***}$ \\\\ \n",
      "  & (1.342) \\\\ \n",
      "  cutrhum_rean2(20,40] & $-$1.655$^{***}$ \\\\ \n",
      "  & (0.615) \\\\ \n",
      "  cutrhum_rean2(40,60] & $-$0.004 \\\\ \n",
      "  & (0.189) \\\\ \n",
      "  cutrhum_rean2(80,100] & $-$0.339$^{**}$ \\\\ \n",
      "  & (0.138) \\\\ \n",
      "  HUMID.NORM & 0.125$^{***}$ \\\\ \n",
      "  & (0.044) \\\\ \n",
      "  cutwind_rean2(5,10] & 0.595$^{***}$ \\\\ \n",
      "  & (0.122) \\\\ \n",
      "  cutwind_rean2(10,Inf] & 1.048$^{***}$ \\\\ \n",
      "  & (0.236) \\\\ \n",
      "  WIND.NORM & $-$0.218 \\\\ \n",
      "  & (0.215) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes \\\\ \n",
      "Date FE & Yes \\\\ \n",
      "State:Month FE & Yes \\\\ \n",
      "Observations & 4,405,171 \\\\ \n",
      "R$^{2}$ & 0.284 \\\\ \n",
      "Adjusted R$^{2}$ & 0.274 \\\\ \n",
      "Residual Std. Error & 77.708 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{1}{r}{Standard errors are in parentheses and are clustered on the state admin level} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# invisible(stargazer(flex_tanomaly,\n",
    "#           type='latex',\n",
    "#           title = \"S6. Main Effects: Binned nighttime minimum temperature anomalies and sleep duration regressions\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Flexible TMIN.Anomaly GHCND', 'Flexible TMIN.Anomaly NCEP Reanalysis 2'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the state admin level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ cuttanomaly_rean2 + CUTPRCP_rean2 +      prcp_norm_7_rean2 + CUTTRANGE_rean2 + trange_norm_7_rean2 +      CUTCLOUD_rean2 + cloud_norm_7_rean2 + CUTRHUM_rean2 + rhum_norm_7_rean2 +      CUTWIND_rean2 + wind_norm_7_rean2 | userID + unique_day_no +      stateyrmn | 0 | state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-384.12  -49.17   -2.05   46.20  403.06 \n",
       "\n",
       "Coefficients:\n",
       "                              Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "cuttanomaly_rean2(-Inf,-10.5]  2.59882      0.57529   4.517 6.26e-06 ***\n",
       "cuttanomaly_rean2(-10.5,-9.5]  1.15455      0.84316   1.369 0.170901    \n",
       "cuttanomaly_rean2(-9.5,-8.5]   1.83146      0.63658   2.877 0.004015 ** \n",
       "cuttanomaly_rean2(-8.5,-7.5]   1.86202      0.55609   3.348 0.000813 ***\n",
       "cuttanomaly_rean2(-7.5,-6.5]   1.67679      0.53655   3.125 0.001777 ** \n",
       "cuttanomaly_rean2(-6.5,-5.5]   1.02782      0.43549   2.360 0.018268 *  \n",
       "cuttanomaly_rean2(-5.5,-4.5]   1.14339      0.37511   3.048 0.002303 ** \n",
       "cuttanomaly_rean2(-4.5,-3.5]   0.72612      0.37116   1.956 0.050420 .  \n",
       "cuttanomaly_rean2(-3.5,-2.5]   1.04344      0.24075   4.334 1.46e-05 ***\n",
       "cuttanomaly_rean2(-2.5,-1.5]   0.41964      0.31263   1.342 0.179497    \n",
       "cuttanomaly_rean2(-1.5,-0.5]  -0.02971      0.17770  -0.167 0.867210    \n",
       "cuttanomaly_rean2(0.5,1.5]    -0.35226      0.18242  -1.931 0.053480 .  \n",
       "cuttanomaly_rean2(1.5,2.5]    -0.63569      0.24141  -2.633 0.008456 ** \n",
       "cuttanomaly_rean2(2.5,3.5]    -0.87748      0.23560  -3.724 0.000196 ***\n",
       "cuttanomaly_rean2(3.5,4.5]    -1.23536      0.20269  -6.095 1.10e-09 ***\n",
       "cuttanomaly_rean2(4.5,5.5]    -1.30746      0.24964  -5.237 1.63e-07 ***\n",
       "cuttanomaly_rean2(5.5,6.5]    -1.69589      0.43856  -3.867 0.000110 ***\n",
       "cuttanomaly_rean2(6.5,7.5]    -1.71088      0.37684  -4.540 5.62e-06 ***\n",
       "cuttanomaly_rean2(7.5,8.5]    -2.24125      0.43309  -5.175 2.28e-07 ***\n",
       "cuttanomaly_rean2(8.5,9.5]    -2.42441      0.38646  -6.273 3.53e-10 ***\n",
       "cuttanomaly_rean2(9.5,10.5]   -3.39253      0.42349  -8.011 1.14e-15 ***\n",
       "cuttanomaly_rean2(10.5, Inf]  -3.68214      0.45330  -8.123 4.55e-16 ***\n",
       "CUTPRCP_rean2(0,1]             0.10311      0.10215   1.009 0.312749    \n",
       "CUTPRCP_rean2(1,2]             0.40721      0.23891   1.704 0.088301 .  \n",
       "CUTPRCP_rean2(2,3]             0.80747      0.33128   2.437 0.014791 *  \n",
       "CUTPRCP_rean2(3, Inf]          1.28681      0.41315   3.115 0.001842 ** \n",
       "prcp_norm_7_rean2             -0.69403      0.84061  -0.826 0.409013    \n",
       "CUTTRANGE_rean2(-Inf,0]       -0.76354      1.16592  -0.655 0.512545    \n",
       "CUTTRANGE_rean2(0,5]           0.65819      0.15160   4.342 1.41e-05 ***\n",
       "CUTTRANGE_rean2(10,15]        -0.94387      0.15008  -6.289 3.20e-10 ***\n",
       "CUTTRANGE_rean2(15,20]        -2.18135      0.27711  -7.872 3.50e-15 ***\n",
       "CUTTRANGE_rean2(20, Inf]      -2.89034      0.85831  -3.367 0.000759 ***\n",
       "trange_norm_7_rean2            0.05284      0.09370   0.564 0.572773    \n",
       "CUTCLOUD_rean2(0,20]           0.78427      0.33140   2.367 0.017956 *  \n",
       "CUTCLOUD_rean2(20,40]          0.89213      0.34972   2.551 0.010741 *  \n",
       "CUTCLOUD_rean2(40,60]          1.29340      0.31633   4.089 4.34e-05 ***\n",
       "CUTCLOUD_rean2(60,80]          1.66830      0.33608   4.964 6.91e-07 ***\n",
       "CUTCLOUD_rean2(80,100]         2.39290      0.34751   6.886 5.75e-12 ***\n",
       "cloud_norm_7_rean2            -0.04682      0.02562  -1.827 0.067655 .  \n",
       "CUTRHUM_rean2(0,20]           -3.72949      0.92250  -4.043 5.28e-05 ***\n",
       "CUTRHUM_rean2(20,40]          -1.83723      0.41270  -4.452 8.52e-06 ***\n",
       "CUTRHUM_rean2(40,60]          -0.22176      0.15497  -1.431 0.152435    \n",
       "CUTRHUM_rean2(80,100]         -0.07149      0.14155  -0.505 0.613527    \n",
       "rhum_norm_7_rean2              0.11493      0.03395   3.385 0.000711 ***\n",
       "CUTWIND_rean2(5,10]            0.43408      0.09354   4.641 3.47e-06 ***\n",
       "CUTWIND_rean2(10,Inf]          0.68539      0.19768   3.467 0.000526 ***\n",
       "wind_norm_7_rean2             -0.16497      0.18007  -0.916 0.359613    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.94 on 7105670 degrees of freedom\n",
       "  (236359 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2758   Adjusted R-squared: 0.2687 \n",
       "Multiple R-squared(proj model): 0.0001335   Adjusted R-squared: -0.009568 \n",
       "F-statistic(full model, *iid*):39.25 on 68941 and 7105670 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 21.39 on 47 and 1256 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #Nighttime Minimum Temperature Anomalies and Sleep Duration FE Regressions - REAN2\n",
    "# flex_tanomaly_rean2 <- felm(formula = sleep_duration_minutes ~ cuttanomaly_rean2 + \n",
    "#                                           CUTPRCP_rean2 + prcp_norm_7_rean2 + \n",
    "#                                           CUTTRANGE_rean2 + trange_norm_7_rean2 + \n",
    "#                                           CUTCLOUD_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           CUTRHUM_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           CUTWIND_rean2 + wind_norm_7_rean2  \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(flex_tanomaly_rean2)\n",
    "\n",
    "# #save model coefficients for plotting\n",
    "# dt <- as.data.frame(summary(flex_tanomaly_rean2)$coefficients[, 1:4]) # extract from felm summary (use df to keep coefficient\n",
    "# write.csv(dt,file=\"model_data/SIFig4B_tempanomaly_rean2_sleep_model_output.csv\")#save data as CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 256428 rows containing non-finite values (stat_bin).”Warning message:\n",
      "“Removed 2 rows containing missing values (geom_bar).”"
     ]
    },
    {
     "data": {
      "image/png": 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KMABDvXasNRUVHanwZOWNMOAAAAfBWA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\nAICJ+D/HsdBAAmfu4AAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot reanalysis2 tanomaly output\n",
    "dt <- read.csv(file=\"model_data/SIFig4B_tempanomaly_rean2_sleep_model_output.csv\",header=T)\n",
    "flex.plot <- binned.plot(felm.est = flex_tanomaly_rean2,\n",
    "                 plotvar = \"cuttanomaly_rean2\",\n",
    "                 breaks = 1,\n",
    "                 omit = c(-0.5,0.5),\n",
    "                 linecolor = \"violet\",\n",
    "                 pointfill = \"violet\",\n",
    "                 errorfill = \"violet\",\n",
    "                 xlabel = \"Min. Temp Anomaly in C\",\n",
    "                 ylabel = \"Change in Sleep Duration (Minutes)\"\n",
    "                 )\n",
    "\n",
    "# hist <- axis_canvas(flex.plot, axis = \"x\") + \n",
    "#         geom_histogram(data = Climate_Controls_DF, aes(x = Climate_Controls_DF$tanomaly_norm_7_rean2), bins=21, fill='gray60', color='gray60', alpha=0.75, size=0.1)\n",
    "# flex.plot <- insert_xaxis_grob(flex.plot, hist, position = \"bottom\", height = grid::unit(0.1, \"null\"))\n",
    "flex.plot <- ggdraw(flex.plot)\n",
    "#ggsave(\"flexplot_tanomaly_rean2.pdf\")\n",
    "flex.plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{S6. Main Effects: Binned nighttime minimum temperature anomalies and sleep duration regressions} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{1}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-2} \n",
      " & Flexible TMIN.Anomaly NCEP Reanalysis 2 \\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " cuttanomaly(-Inf,-10.5] & 2.599$^{***}$ \\\\ \n",
      "  & (0.575) \\\\ \n",
      "  cuttanomaly(-10.5,-9.5] & 1.155 \\\\ \n",
      "  & (0.843) \\\\ \n",
      "  cuttanomaly(-9.5,-8.5] & 1.831$^{***}$ \\\\ \n",
      "  & (0.637) \\\\ \n",
      "  cuttanomaly(-8.5,-7.5] & 1.862$^{***}$ \\\\ \n",
      "  & (0.556) \\\\ \n",
      "  cuttanomaly(-7.5,-6.5] & 1.677$^{***}$ \\\\ \n",
      "  & (0.537) \\\\ \n",
      "  cuttanomaly(-6.5,-5.5] & 1.028$^{**}$ \\\\ \n",
      "  & (0.435) \\\\ \n",
      "  cuttanomaly(-5.5,-4.5] & 1.143$^{***}$ \\\\ \n",
      "  & (0.375) \\\\ \n",
      "  cuttanomaly(-4.5,-3.5] & 0.726$^{*}$ \\\\ \n",
      "  & (0.371) \\\\ \n",
      "  cuttanomaly(-3.5,-2.5] & 1.043$^{***}$ \\\\ \n",
      "  & (0.241) \\\\ \n",
      "  cuttanomaly(-2.5,-1.5] & 0.420 \\\\ \n",
      "  & (0.313) \\\\ \n",
      "  cuttanomaly(-1.5,-.5] & $-$0.030 \\\\ \n",
      "  & (0.178) \\\\ \n",
      "  cuttanomaly(.5,1.5] & $-$0.352$^{*}$ \\\\ \n",
      "  & (0.182) \\\\ \n",
      "  cuttanomaly(1.5,2.5] & $-$0.636$^{***}$ \\\\ \n",
      "  & (0.241) \\\\ \n",
      "  cuttanomaly(2.5,3.5] & $-$0.877$^{***}$ \\\\ \n",
      "  & (0.236) \\\\ \n",
      "  cuttanomaly(3.5,4.5] & $-$1.235$^{***}$ \\\\ \n",
      "  & (0.203) \\\\ \n",
      "  cuttanomaly(4.5,5.5] & $-$1.307$^{***}$ \\\\ \n",
      "  & (0.250) \\\\ \n",
      "  cuttanomaly(5.5,6.5] & $-$1.696$^{***}$ \\\\ \n",
      "  & (0.439) \\\\ \n",
      "  cuttanomaly(6.5,7.5] & $-$1.711$^{***}$ \\\\ \n",
      "  & (0.377) \\\\ \n",
      "  cuttanomaly(7.5,8.5] & $-$2.241$^{***}$ \\\\ \n",
      "  & (0.433) \\\\ \n",
      "  cuttanomaly(8.5,9.5] & $-$2.424$^{***}$ \\\\ \n",
      "  & (0.386) \\\\ \n",
      "  cuttanomaly(9.5,10.5] & $-$3.393$^{***}$ \\\\ \n",
      "  & (0.423) \\\\ \n",
      "  cuttanomaly(10.5,Inf] & $-$3.682$^{***}$ \\\\ \n",
      "  & (0.453) \\\\ \n",
      "  cutprcp(0,1] & 0.103 \\\\ \n",
      "  & (0.102) \\\\ \n",
      "  cutprcp(1,2] & 0.407$^{*}$ \\\\ \n",
      "  & (0.239) \\\\ \n",
      "  cutprcp(2,3] & 0.807$^{**}$ \\\\ \n",
      "  & (0.331) \\\\ \n",
      "  cutprcp(3, Inf] & 1.287$^{***}$ \\\\ \n",
      "  & (0.413) \\\\ \n",
      "  PRCP.NORM & $-$0.694 \\\\ \n",
      "  & (0.841) \\\\ \n",
      "  cuttrange(-Inf,0] & $-$0.764 \\\\ \n",
      "  & (1.166) \\\\ \n",
      "  cuttrange(0,5] & 0.658$^{***}$ \\\\ \n",
      "  & (0.152) \\\\ \n",
      "  cuttrange(10,15] & $-$0.944$^{***}$ \\\\ \n",
      "  & (0.150) \\\\ \n",
      "  cuttrange(15,20] & $-$2.181$^{***}$ \\\\ \n",
      "  & (0.277) \\\\ \n",
      "  cuttrange(20,Inf] & $-$2.890$^{***}$ \\\\ \n",
      "  & (0.858) \\\\ \n",
      "  TRANGE.NORM & 0.053 \\\\ \n",
      "  & (0.094) \\\\ \n",
      "  cutcloud_rean2(0,20] & 0.784$^{**}$ \\\\ \n",
      "  & (0.331) \\\\ \n",
      "  cutcloud_rean2(20,40] & 0.892$^{**}$ \\\\ \n",
      "  & (0.350) \\\\ \n",
      "  cutcloud_rean2(40,60] & 1.293$^{***}$ \\\\ \n",
      "  & (0.316) \\\\ \n",
      "  cutcloud_rean2(60,80] & 1.668$^{***}$ \\\\ \n",
      "  & (0.336) \\\\ \n",
      "  cutcloud_rean2(80,100] & 2.393$^{***}$ \\\\ \n",
      "  & (0.348) \\\\ \n",
      "  CLOUD.NORM & $-$0.047$^{*}$ \\\\ \n",
      "  & (0.026) \\\\ \n",
      "  cutrhum_rean2(0,20] & $-$3.729$^{***}$ \\\\ \n",
      "  & (0.923) \\\\ \n",
      "  cutrhum_rean2(20,40] & $-$1.837$^{***}$ \\\\ \n",
      "  & (0.413) \\\\ \n",
      "  cutrhum_rean2(40,60] & $-$0.222 \\\\ \n",
      "  & (0.155) \\\\ \n",
      "  cutrhum_rean2(80,100] & $-$0.071 \\\\ \n",
      "  & (0.142) \\\\ \n",
      "  HUMID.NORM & 0.115$^{***}$ \\\\ \n",
      "  & (0.034) \\\\ \n",
      "  cutwind_rean2(5,10] & 0.434$^{***}$ \\\\ \n",
      "  & (0.094) \\\\ \n",
      "  cutwind_rean2(10,Inf] & 0.685$^{***}$ \\\\ \n",
      "  & (0.198) \\\\ \n",
      "  WIND.NORM & $-$0.165 \\\\ \n",
      "  & (0.180) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes \\\\ \n",
      "Date FE & Yes \\\\ \n",
      "State:Month FE & Yes \\\\ \n",
      "Observations & 7,174,612 \\\\ \n",
      "R$^{2}$ & 0.276 \\\\ \n",
      "Adjusted R$^{2}$ & 0.269 \\\\ \n",
      "Residual Std. Error & 77.939 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{1}{r}{Standard errors are in parentheses and are clustered on the state admin level} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# invisible(stargazer(flex_tanomaly_rean2,\n",
    "#           type='latex',\n",
    "#           title = \"S6. Main Effects: Binned nighttime minimum temperature anomalies and sleep duration regressions\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Flexible TMIN.Anomaly NCEP Reanalysis 2'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           covariate.labels = c(\"cuttanomaly(-Inf,-10.5]\",\"cuttanomaly(-10.5,-9.5]\",\"cuttanomaly(-9.5,-8.5]\",\"cuttanomaly(-8.5,-7.5]\",\"cuttanomaly(-7.5,-6.5]\",\"cuttanomaly(-6.5,-5.5]\",\"cuttanomaly(-5.5,-4.5]\",\"cuttanomaly(-4.5,-3.5]\",\"cuttanomaly(-3.5,-2.5]\",\"cuttanomaly(-2.5,-1.5]\",\"cuttanomaly(-1.5,-.5]\",\"cuttanomaly(.5,1.5]\",\"cuttanomaly(1.5,2.5]\",\"cuttanomaly(2.5,3.5]\",\"cuttanomaly(3.5,4.5]\",\"cuttanomaly(4.5,5.5]\",\"cuttanomaly(5.5,6.5]\",\"cuttanomaly(6.5,7.5]\",\"cuttanomaly(7.5,8.5]\",\"cuttanomaly(8.5,9.5]\",\"cuttanomaly(9.5,10.5]\",\"cuttanomaly(10.5,Inf]\", \"cutprcp(0,1]\", \"cutprcp(1,2]\", \"cutprcp(2,3]\", \"cutprcp(3, Inf]\", \"PRCP.NORM\", \"cuttrange(-Inf,0]\", \"cuttrange(0,5]\", \"cuttrange(10,15]\", \"cuttrange(15,20]\", \"cuttrange(20,Inf]\", \"TRANGE.NORM\", \"cutcloud_rean2(0,20]\", \"cutcloud_rean2(20,40]\", \"cutcloud_rean2(40,60]\", \"cutcloud_rean2(60,80]\", \"cutcloud_rean2(80,100]\", \"CLOUD.NORM\", \"cutrhum_rean2(0,20]\", \"cutrhum_rean2(20,40]\", \"cutrhum_rean2(40,60]\", \"cutrhum_rean2(80,100]\", \"HUMID.NORM\", \"cutwind_rean2(5,10]\", \"cutwind_rean2(10,Inf]\", \"WIND.NORM\"),\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the state admin level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Supplementary Figure 4: Alternative (gridded NCEP-R2) meteorological data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ CUTTMIN_rean2 + tmin_norm_7_rean2 +      CUTPRCP_rean2 + prcp_norm_7_rean2 + CUTTRANGE_rean2 + trange_norm_7_rean2 +      CUTCLOUD_rean2 + cloud_norm_7_rean2 + CUTRHUM_rean2 + rhum_norm_7_rean2 +      CUTWIND_rean2 + wind_norm_7_rean2 | userID + unique_day_no +      stateyrmn | 0 | state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-383.35  -49.16   -2.04   46.20  404.19 \n",
       "\n",
       "Coefficients:\n",
       "                          Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "CUTTMIN_rean2(-Inf,-20]    4.29773      0.80945   5.309 1.10e-07 ***\n",
       "CUTTMIN_rean2(-20,-15]     3.40395      0.54774   6.215 5.15e-10 ***\n",
       "CUTTMIN_rean2(-15,-10]     1.50812      0.41763   3.611 0.000305 ***\n",
       "CUTTMIN_rean2(-10,-5]      1.40131      0.31759   4.412 1.02e-05 ***\n",
       "CUTTMIN_rean2(-5,0]        0.72697      0.25946   2.802 0.005080 ** \n",
       "CUTTMIN_rean2(0,5]         0.64388      0.16401   3.926 8.64e-05 ***\n",
       "CUTTMIN_rean2(10,15]      -1.06051      0.22918  -4.627 3.70e-06 ***\n",
       "CUTTMIN_rean2(15,20]      -3.65023      0.25665 -14.223  < 2e-16 ***\n",
       "CUTTMIN_rean2(20,25]      -6.17590      0.45556 -13.557  < 2e-16 ***\n",
       "CUTTMIN_rean2(25,30]      -8.66626      0.60645 -14.290  < 2e-16 ***\n",
       "CUTTMIN_rean2(30, Inf]   -11.53628      1.37964  -8.362  < 2e-16 ***\n",
       "tmin_norm_7_rean2         -0.04603      0.06106  -0.754 0.450959    \n",
       "CUTPRCP_rean2(0,1]         0.08704      0.10113   0.861 0.389401    \n",
       "CUTPRCP_rean2(1,2]         0.48855      0.23295   2.097 0.035974 *  \n",
       "CUTPRCP_rean2(2,3]         0.95143      0.33803   2.815 0.004883 ** \n",
       "CUTPRCP_rean2(3, Inf]      1.32472      0.41192   3.216 0.001300 ** \n",
       "prcp_norm_7_rean2          0.03896      1.08692   0.036 0.971409    \n",
       "CUTTRANGE_rean2(-Inf,0]   -0.95896      1.14869  -0.835 0.403813    \n",
       "CUTTRANGE_rean2(0,5]       0.51467      0.13837   3.720 0.000200 ***\n",
       "CUTTRANGE_rean2(10,15]    -0.76598      0.14312  -5.352 8.69e-08 ***\n",
       "CUTTRANGE_rean2(15,20]    -1.92065      0.27438  -7.000 2.56e-12 ***\n",
       "CUTTRANGE_rean2(20, Inf]  -2.70150      0.81545  -3.313 0.000923 ***\n",
       "trange_norm_7_rean2       -0.16723      0.08201  -2.039 0.041428 *  \n",
       "CUTCLOUD_rean2(0,20]       0.65779      0.32720   2.010 0.044394 *  \n",
       "CUTCLOUD_rean2(20,40]      0.70697      0.34370   2.057 0.039691 *  \n",
       "CUTCLOUD_rean2(40,60]      1.06253      0.30997   3.428 0.000608 ***\n",
       "CUTCLOUD_rean2(60,80]      1.41423      0.32799   4.312 1.62e-05 ***\n",
       "CUTCLOUD_rean2(80,100]     2.16376      0.34301   6.308 2.82e-10 ***\n",
       "cloud_norm_7_rean2        -0.02802      0.02321  -1.207 0.227264    \n",
       "CUTRHUM_rean2(0,20]       -2.88031      0.88929  -3.239 0.001200 ** \n",
       "CUTRHUM_rean2(20,40]      -1.53665      0.41533  -3.700 0.000216 ***\n",
       "CUTRHUM_rean2(40,60]      -0.14592      0.15432  -0.946 0.344353    \n",
       "CUTRHUM_rean2(80,100]     -0.07858      0.13112  -0.599 0.548996    \n",
       "rhum_norm_7_rean2          0.07785      0.02857   2.725 0.006432 ** \n",
       "CUTWIND_rean2(5,10]        0.30560      0.09412   3.247 0.001166 ** \n",
       "CUTWIND_rean2(10,Inf]      0.43011      0.18408   2.337 0.019463 *  \n",
       "wind_norm_7_rean2         -0.17366      0.16514  -1.052 0.292982    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.94 on 7105680 degrees of freedom\n",
       "  (236359 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2758   Adjusted R-squared: 0.2688 \n",
       "Multiple R-squared(proj model): 0.0001789   Adjusted R-squared: -0.00952 \n",
       "F-statistic(full model, *iid*):39.26 on 68931 and 7105680 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 31.93 on 37 and 1256 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #Binned FE Regression REAN2 - tmin\n",
    "# flex_t_binned_rean2 <- felm(formula = sleep_duration_minutes ~ CUTTMIN_rean2 + tmin_norm_7_rean2 + \n",
    "#                                           CUTPRCP_rean2 + prcp_norm_7_rean2 + \n",
    "#                                           CUTTRANGE_rean2 + trange_norm_7_rean2 + \n",
    "#                                           CUTCLOUD_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           CUTRHUM_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           CUTWIND_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(flex_t_binned_rean2)\n",
    "\n",
    "# #save model coefficients for plotting\n",
    "# dt <- as.data.frame(summary(flex_t_binned_rean2)$coefficients[, 1:4]) # extract from felm summary (use df to keep coefficient\n",
    "# write.csv(dt,file=\"model_data/SIFig4_binned_tmin_sleep_duration_rean2_model_output.csv\")#save data as CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 181414 rows containing non-finite values (stat_bin).”Warning message:\n",
      "“Removed 2 rows containing missing values (geom_bar).”"
     ]
    },
    {
     "data": {
      "image/png": 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G8AAFRsBflAT4eDEEIIIWfUOrEbOXJk\nVaeKioqqOVsVb2/v2l7iNLulibt06TJ27NioqCi9Xn/69OmvvvrKmvaVlpb+8ssvdrulsSxb\nWVlpOTabzdY+6CZAa1hEEBopvbs4851gtaejQQghhJBTaj3Gzu3ZTLdu3dLS6mm93FOnTv32\n22+WY5lMNnfuXNsNM65du/bcc8/xPG95GRoaum7duqpu1ZTG2FmRZCHPBwHOqEAIIYQaJ8//\n/I6Li6u3Z3Xt2rVr165VnW3dunWvXr2Sk5MtL3NzcysqKuqzQdHjLFkd4IRZhBBCqHHyzOSJ\n248nyRdffNGzMdiyW9mutLTUU5E0BLfbC7V7gL/72ESEEEIIeZYnEztfX99vvvkmKirKgzHY\nsdvrliQ9nPh6XF4SW3T8AmSPhauxoN3t6XAQQgghVJ1a97WJFxNetmyZ5UCpVM6aNasmNwkI\nCIiKikpMTKzP/Vj1ev17771nfZmYmCie6pGZmWn70s/Prz4ia9iUXm+AoAc2G0xXQOHpaBBC\nCCFUtVondh9++KFdiTWx8/X1FZ9tOGQy2fnz581ms+WlTqcbMWKE7VyQzMzM48ePW1+2atWq\nQW2G5inl2i84r0iGOlqS8bQ6wdPRIIQQQqhqzairkSCIbt26WV9evnx52bJlN27cYFm2pKRk\n//79ixcv5jjOWuGee+7xRJgNjgDySv17pZo9ACTuMIsQQgg1ZG6Y9mhdbVihaOgddePGjTtx\n4oT15eHDhw8fPuywplqtvvfee+srrkZAgFvrwuCEWYQQQqjBcsOP5zlz5rh+k/rRqVOnSZMm\nfffdd9VXU6lUr732mu0Sd8hOXhJ7K7crXQOqh4DGRY0RQgghz2tGXbEWjzzyyKxZs6RSaVUV\nYmJiVq5cGRERUZ9RNUZ5SWzpyV8gfw5ciwHNLk+HgxBCCCGXW+xefvll14Po0aPH+PHjXb9P\nDd17772JiYl//fVXWlrajRs3NBqNTCbz9/ePiYnp169fly5d6i2Sxk4uXQ0AwJUC2YyWcUYI\nIYQarFpvKWZ/vTt2GJs2bdrGjRtdv099apJbitUWQRgV0vdIorxC/xGOukMIIYQ8Dn8YI+cJ\nglRjeNNyfHvUHUIIIYQ8pNmNsUN1Jy+JbeBNjAghhFDTholdQ3Tx4sVp06Z17Nix00j16KcG\nrN2ywmw2eTqomrqd2wkGjwaCEEIINTuY2DU4hw4d6tmz58UfK+8pXvi011dROWPXrVv38P+N\nNBj1ng6tpvKSWChZCde6A5vn6VgQQgihZsTVQVG//vprDWvqdLpz587t3r3bum2XUqn8+OOP\ng4ODw8PDXQyjyTAajVOmTBnBzBvtu8BSEi3rl6ic/Na5oUvf++C1xa+TlGcDrBEvZjMUzAcA\nyB4FkSeAwLF3CCGEUH1wdVZsbfE8/9FHHz3//POWlx06dDh06JBa3fiWt62jWbF79uwZN2ry\nqogMimBsy49qvt9Z9v6q2PTQAXTL4bQsoEE3tRJEub9iFEOlQthX4P2op8NBCCGEmov6zg9I\nkpw/f/7EiRMtLy9duvTEE0/UcwwN2b///tuC6WCX1QFAS0nnQjZTX8Ze+9n89//pUz8wFJ/l\noF5z8loQBJ9S7W+lup155yd6OhaEEEKoGfFMw89zzz1nPf7999/37NnjkTAaIIVCoRcqxOU6\nvkJCyEigAEDgoTCNO/mu4Z/ndZm7zZyxIeZ3vOBnNI+EBr8UH0IIIdSUeCaxi4mJsV3ZeMuW\nLR4JowHq169frinjpvmSXfkJ3c4Osr52hdqbwsWvTQdn6899adRk8/UVY61hbocQQgjVD88k\ndnb7VVinU6A2bdpMnf74JwWPW3M7Afj9lesPVG4c67vQ4SWsXsjexx5ZoE9+Q5+XzApcPYZb\nY5jbIYQQQvXAM9MV09PTbSdt5ObmeiSMhmnNmjVPc0+/+nXfUEmUNxWUY7rICqb/BW1oK+1V\n/YVlGXxZhlEWYI4YRocPpiXebtjtzY1ubU2h+Rm8+gPl5+lwEEIIoSaovmfFAoBerx84cOCJ\nEyesJV5eXjqdrp7DcFFd7xV7+fLl5OTkf0/ktQqICi2OLzwgM1fW4jtF0BDSgw4fSgfENqD1\nUWTM976KqSDrAhF7gfL3dDgIIYRQU+Nqi92hQ4dqWFMQhIKCgosXL65bt+7mzZu2p4KDg10M\no+lp3759+/bt89rd6sHkJ0BBCpu521yWUaOxdAILeclsXjLr3ZqMGMK0SKQomccb8MxK2VIA\nDgzpoN0D3pM8HQ9CCCHU1Lia2A0aNMj1IHr37u36TZo2kgZ1PK2Opyuu8ln7zTf/4Wo4Gbbi\nGn/uS+Olb4mQPlSrURJFmAfTO6ZE+6e/YrjO9KQ3ZnUIIYRQHWgQWwKMGzfO0yE0Gt5tyJg2\n0qhHhJxDbOZus76wRumdWSdk72NzDrD+najwoXRIL5rwxLQZng8q1iQJglRnGW+HEEIIIbfy\n/A/X7t27T5gwwdNRNDK0nIgcxbQcwZSc5zJ/Nxem12ixYoGH4rNc8VlOHmIKv4cJH0wzqvpu\nwBMEqeUgD3M7hBBCyN08/JO1ZcuWO3bsIMkGvUFWg0WQEBBLBcRSunwhe785ez9r1tSoAU+X\nL1zaarrygym4B93yXsavg2f+/jG3QwghhNzLYxmVVCp98skn09LSIiMjPRVDkyEPITpMlgxa\nI+/8P4l3ZE2/pzwLecns8df1x17RZ+9jeZMHdrDA9e0QQgghN3K1vWTWrFm1qi+Tyfz9/Tt3\n7jxgwAB/f1zwwp1IBkL7M6H9GcsEi9y/Wd5cowsrrvLnrhovbyPCBtERQ2mvoHpN9/OSWHXn\nvVCyDMJ2AKmqz0cjhBBCTYwH1rFrGup6HTsLVxq0jOVC7iH2xh7WUFyL3cYIEvw7US1H0cHd\naaiXAXgSeq+fYixBGEE+ACL+AkJSH09FCCGEmiIc4dRkSX2I1qOZVvczhensjd1s8bnaTbBQ\nqE1hg5nwe2hGWbf5HcdH80I4RfwLXn0wq0MIIYRcgYldE0eQEBxHB8fRuptC9kFz1j6W1dao\njVabJ1zaarryo0mdQEfex9R86F5tcXx4SeVfUsku3b9z1UF19BCEEEKoWcCuWCc1/K5Yh1iD\nkHeEu/GnuTKrFv2zAODdhowcSav7MmQdb1GG82QRQgghp7nth2haWtpff/11+vTp4uJig8FQ\nq2sPHDjgrjBQ9WgZET6EDh9Cl2VwmbvZgpMsz9Xowoqr/Jk1pkvfmkMH0C2H07KAumrAwzVQ\nEEIIIae54SfopUuXZs6c+ffff7t+K1RvfKMo3yjKWCrJPcxm/skaS2rUgGcsE679bL7+qzmw\nKxV5LxMQQ9XFBAvM7RBCCCHnuPrjMyUlZdCgQRqNxi3RoHom9SNaj2Za3cvkp7DZe9niszVq\nvhN4KEzjCtM4RQsiYhgTfg9NSd2c393K7YynQBIFhMy9N0cIIYSaKpcSO4PB8NBDD2FW19gR\nNKjjaXU8rc0Rsvaasg9ynKFmEyxuChe/Nl3ZZlYnUpEjGWW4O/tni08eDvC+H7wSIHwX5nYI\nIYRQTbiU2G3atOnGjRvuCgV5nCKMiJ4qbfewkHeUy/zDpMmuUXrHGoTsfezpP28cZj7LotI0\nfHH7Vh3vv+eh0UNc2AKY4HzkTwNfAdo9ULYB/P7n/K0QQgihZsOlxG7Xrl3uigM1HLQXET6E\nDhtMF5/hbvxpLjrFCXcbgHfVmLI8f3ykpEuc/D4F5ZeVcm7+wTl/Hv71k8UbndwIWKBKNb/4\nK4cY2aFyv9rtboIQQgg1Wy4ldqdOnbIr6dmz5/PPPx8dHR0SEuLkT3TUMBAkBHalArtS+gIh\na685+yBrrnTcgMcKxrWFMwappk7we8NS0kcBQ1RPvrV36JZu66eMnelcABwfWVx5hBeCK5J4\ndQK+lxBCCKG7cymxKy4utn05YsSI3bt3E0S9bESF6otXMNHhEUm7hyUFKWzmbnNZhn3z3XnD\n3wZB86DvItvCADriPp/nvv7B+cQOAHghxHKA82QRQgihmnCpIUSpVNq+fPfddzGra6pIGtTx\ndPwbXgnveIX2Z0jm9qk885UIJpYW7QbWVtrj8vULNZxpe1duX6sZIYQQanpcSuxCQ0OtxwRB\nxMTEuBwPauh82pKd/ycZvE4e/bjEK5gAAIpgzIJRXNMkGGiQpiwxZB9wT06GuR1CCCFUPZcS\nuyFDhliPBUHAdU+aD1pORI5i+q+U93xF1rtH/DVTWjmXb1cnTfd7W2kvgYdzXxiv/Ghyy3Pz\nklgwnoW8p0Awu+WGCCGEUFPiUmI3Y8YM2xkS4rkUqGkjSAiIpca/mdCn28C1hTMqudtjLpO1\nP+2v3HC/z3MAAAL8u918YaPxrrNr74qmLvJXh0HZF5A7CXM7hBBCyI5LiV2XLl1eeukl68s3\n33xTEGq08hlqYj5//xv/TvSCnK4r8id8XjTr1dw+64vmzAhY1UHWx1rnxh42/SMjb3LxHULC\nrV3McJ4sQgghZI9wMRXjOO6pp57asGGD5eXMmTOXL1+uUqncEVuDtmvXrqlTp5aVldXpUxrR\nqDJBEI6l/X0yLfnC3vwATVRXr+HeVJC4mk87Mu5FmUTl/CQbmjwvl35aoV+lTsDtKBBCCKE7\nuJTY7dmz58yZM4IgbNy48fz585bCFi1aDBo0qGPHjnK5vIb3ef75552OwVMwsasKZxROf2wq\nSK0ycnkI0eNlL3mIGyZQ4xooCCGEkC2XEruZM2d++eWXrgfRGDtwMbGrhsDDhU3GrL+qDF7q\nS8QtkHm3cUN3KuZ2CCGEkBUOVELuR5DQaYY0+nEJVNEqZywTjr9lKEp3wxJ3jTT3RQghhOoC\nJnaorkSOYjrPlpCU47OcUUj90JC93w1p2a3cjs0HcM9iyAghhFAjhYkdqkOh/Zm4l2S0l+OG\nO4GHc1+6Z4m7wuNXIbMv5D6GuR1CCKHmDBM7VLcCYqneb8ik/lW80wT4d7v53GdGwaV8TPBV\njAPzVaj4Dko+cuVGCCGEUKPm0sDz6dOn9+vXz12hoKZK1ZKMf12WslSvzXU8Syb7IGuqhC7P\nSCipc1NliQrdl37K4SwXK/Gd5UqoCCGEUKPm6jp2zRbOiq0ts0ZIXWYsy6iyac6nLRm30Pkl\n7mjyPCe0FgQvnCeLEEKo2cKuWFRPGCXR61WZOr7KrKv8Xz75Nb02z8nfNFi+kyB4QdPKhhFC\nCKFawcQO1R+Shi7/J40YXmVup8sXTryhr7jq6p6ymNshhBBqnjCxQ/WKIKHT9GqXuCsXjr9t\nKEx1dXIr5nYIIYSaIUzskAdEjmI6z5ZWucSdQUhbbsje52pmlpfEAlcIxUsAcCApQgihZqGm\nw8wfeOAB25fh4eFr164FgOnTp7sexMaNG12/CWpcQvvTUj8ifYWR1TvIuixL3OkK+A6TJU4/\ngiQL2YvDaeosmK+B+jOoqpEQIYQQaipqOiuWIO74oRgdHX3hwgVxuXMa48zc+pkVq01/Qcr8\nwXKdy/WrBcGnTp/lEZVZfMr7RmNJlYPqwgbRMU9KiSra9qrHUMf9lSMJohJkPaDlQSCVzgeK\nEEIINQbYFdugKQLSaeqcVPKzAI6TEgK09RySe6kiyPg3pIrQKn89yDnIpnxgYA3OpP5mrnep\n5mcTO7AgfzdmdQghhJoDTOwaNjoMmEhCFqOOl6oTaMt/1pMkURTi6xekipJLV3swRhd5BZEJ\nb3n5RVfZKFd8mjv5tsFU4UxuZ+L6lWj28oIfzqVACCHUHGBi17C1+AraXofIf2zLrBlecOcL\nAAJFXSXA8XarFJlDEkX1EqhLaAXR8xVZNQsLl1/lkxfrdTdd6rLH3A4hhFCTh4ldY0BUMYGA\nVIBqAkiiVB16WLM92/RIIX032KdFsE9Ew0/vSBq6PCNtWe0Sd0mv60svubTEHeZ2CCGEmraa\nzoq9fv267UuGYSwHSUlJ7g0I1YKsN4RtExffzu0yz4EeSNrIC4EObyCld7N8J46PrLsYa44g\noeN0qVxNZmw2CY7yN3OlkLLE0PX/pEHdnZpMAQAAeUmsOoEGwQCEzPlYEUIIoQappoldZKTj\nn/3x8fHuCwa5m89jIGkPhETd/o5vtKXhiiKy/JSjAUBnnFOh/8gzEYpEjmJk/tt/fIIAACAA\nSURBVMTpT4282cFZziCkLTN0miENH+L8hrD5SeUhoWNA1hOClzkfKEIIIdTw4HbpTZrvbPCd\nLS6+1aSnOQ/ZAADylp0qMhxcLaGPkGQuy3Vh+XYgON9IVlsh8XScgji10mjWVbvE3aQqt6+o\nnp/yIdAdAt0hoMPA/zlXw0UIIYQaDEzsmjFpZ1CvBcMpkCeKJy7kJbFekvVekm8AoKA8j4eA\n+gwtIJbq/abs5JIql7i79rPZVC50mlnl9hXV0BgW+CmOcUI47f2wq4EihBBCDQlOnmjGmJbg\nOwvUa0HaVXxSnUB7eZ8BAKBDg+NDHFxOcCrZS16Sr2nqUl1Epwwn+7wtVbWs8i2ac4hNW+rM\nEncmdmip9ueSyn15Jx19XQghhFCjVdOdJwBg+/btdRTEuHHj6ujOdad+dp7wMOMpMKSDYALf\nmeKTRSfOB6piAEBrfLZSX1eD1VitkLrMWHqRq6qCTxuy+4syqY/zO6BUs8wKQggh1LjUIrFz\ny+5hDuGWYo1S5Q+Q8zAAQIuN4DOt7lYS4Vk4s8aYd6zK+3sFEz1e8lK0cEduV/Y56I+CpB34\nPQukyukbIoQQQh6BbRXIWaqHoM1FMJ4GWQLc2e5lSfIIMAlQxQp8tUHS0PUZqVcAce1XRxNl\nAfQFQvJifdwLMt8oJ4cWWLNSdcQ+qNwGQIL/846rGlKBaQWUv3MPQgghhOoUjrFDTqNAEgWq\nCcBE2J1QJ9AkURLo3UUh/dA9jyKgw6OS6MclRBVvWLNGOLnEUJDqaquhqTQfADghLC+ZyUti\nLf/dPi3o4XpPuBwAN6e5+CCEEEKoLmCLHaoLfHDoFND+q/JaJAh+OtOTbrnpXZa4Mwrpy40d\np0PEUOff1SWavSRRRpIFtoXW3I6mLgWqBADQ5rdQtHB0veZnqPgOJO3A92mgw5wOAyGEEHIO\nJnaoLpDg/SjoDoOknb7sETfeNySe7qEk0ldUucTd+fVGfaHzS9wBAC/48pyvw1OC4FNpeJsm\nr5i4xEpHYwrVbY9AxVYAAO8qvmrjaaBCgMbZuAghhOqES4kdRVF+fn7uCgU1KT6Pg7QjkD4h\nrb3dO6/CP4bq/aYs5X2jobjKJe6MZRDzlMSJJe6qx/HhWsNL1VTQ5xZ4SQEEKj8tQhD+G7dn\nO+s2aySwN0E+AFoecnNwCCGEkIuJHcdxCoUiMTGxb9++iYmJnTt3pqj6258ANXSyXpY/1Qm0\ne3M7ZTgZ/7Y0bamxItNxbpf7t9lYwnebL6W96moqt0Pl+i8q9B9R5A1BkFoLrV87QWhCfPIA\nwFAWJmvp6HrDcSj+ECTtwHsKSDvVR8QIIYSaFncud6JSqRISEhITExMTE+Pj41WqprxaBC53\n4gT3pnesTkhdZiy9UOUSd6qWZI+FUql/Q5khRBDlcskXFHXFzPbVmx4XV1BHfwl5swEAIn4H\nxSgHtzBdAEIJTDg43dOMEEKoSaurdewoiurSpUvifyIi7CdONnaY2DknL4mlyQsUlWU0D3f9\nbjwLZ9cabx6twyXu6pNS9o5S9jYAX1hxgePbWQrv6MnNTAT9UaDDoV2WZ0JECCHUsNUisTt5\n8uTRo0ePHTt27NixzMzMWj0mPDzcmuR17dq1CfTYYmLnJF7DXuxNkxe1xhcqDW+D4PI7QYBL\nW03XfnG8xB0AMEoi7gWpb1TjeMsRhJEirrJ8e4fDJIK9Q0my0MwmMLHH6j82hBBCDV8tEjtb\nN2/ePPaflJQUg8FQ82uVSmV8fLwlyUtISPD29nYiAI/DxM5JFVsgdwqAYDTfV6rd4a4uxczd\n5ozNJsHxiDsgGegyRxoS39jngAsK6ScUdZnjW2kNt9dPvt2kxxUBrxcvK4gQQqj5cDKxs2Uy\nmdLS0qx5XlZWLTqJSJLs3Llzenq6izHUP0zsnKfZBYWLoeX+vBM+brxrfjJb1RJ3AECQ0HGq\nNGJ4Y8/tqqROoKH4XShcDIp7QP05MK09HRFCCCEPcENiZycnJ8eS4R09ejQtLc1oNN71Etwr\ntvnhLbueuHc6Rcl5Lm25kXW0xJ1F5Eg6+nFpU514EKiKpakMIJXQ7iaQSk+HgxBCyAPcn9jZ\nMhqNqamp1sa8nJwch9UwsWvm3JjeabL4lKVVLnEHAKH9mZin3b/EnecRnBfzrZdkM8dHlus+\nB7tZFwghhJqHuk3sbGm12h9++GH58uVnz561O4WJHbLkdgSYBJC4eCtDKZ+61FhZxRJ3ABAQ\nS9X/Enf1iLfdA/p2emc8DfojoJoIlL9n4kIIIVT36jaxy8nJOfKf9PR0jnO85BgmdggACpOv\n+XsP1BgW643TXLwVaxDSVxqLT1e9xF0E2eOlBrTEXT1Qt/o/KFsLhBRanQRprKfDQQghVCfc\n3FnD8/yZM2esyVxtV0VBzZdgClJPBn2Wj9dMgfcxmB905Wa0jOixQHam6iXuKrP45DcMPV7y\nUoQ21Xa7OxEcX7qDJIDjQijc0wIhhJouNyR2Go0mOTnZkskdO3assrKy5teGhoYmJia6HgNq\n9AgGvCeDIRVkPY1l97nhfjR0mSuVtyD+3e54oqy+UEharI97QeoX3fQG3IkIVFFFmkzyPQhS\nXRIPwAMOwkMIoabIya7Y7Oxsa7PcqVOnqupjdfA8gujUqVNiYmK/fv369evXunVjXZQBu2Lr\nhO4gSDoAHerG6RSZf7AZ3xirXOKOhs5zpM05xbn1tWt2Qela8HkcVA8C4eXpoBBCCDmpFj/P\n0tPTrcncjRs3an6hVCrt1auXJZnr27evvz+O3UZVkA+y/KlOoN2V20WOpL0C4NRqE2+y/x2G\nE8wpFX9uezNV2l4TNyBmZP8HQkOa3eq+lr9nP8UGKfMnaP+ENhdAEu3poBBCCDmprvaK9ff3\nt+wt0a9fv549e0qlUqfCa7iwxa5+uCu9K73ApS43strb7/YSNndlwcPlXEFHWT8F6ZdpOp3F\nnXll0kfjBk5j5AQlJyipQEkJRt4cBuHx/sr7JPR+M9ezuPJIc26/RAihxs6dn+Bt2rTp16+f\nJZnr2LFjrRJBhByyNN0x1HEgODPbx+n7+HWk4l+XWZe4E4D/pPCxIDrytRZ/Sf7reUzV/fbO\nlieMeyM7yO54EO1FUDKgJEArCNqLoCRAyQhaDrQXQUmBkhGMF0F5ASUDSkI0zqSQLNHspshM\nkiiC/5LpO9I7XgMCC5Svp+JDCCFUQy4ldhRFdevWzZrMtWjRwl1hIWSl7lXBZTxCkTmV+re0\nxhec3l5WGUEmvC1L+cBQeZ2/aPgnz3z5xZCdEpvxZHHy+wappu2u+NgusWP1Aqu3HNZ6QCrJ\nACkhKAkwCqAVBMUQJAOMkmAUQEoIkgFGQTDKW8cSBUHLCVJCkBJB6k0SjiZ1mM2mPw//cjoj\ntVJbEdU6ZuTA0erA0NpG5RDHR3IQaX1pbStVJ9BQvgkKFoByNAS/j5uVIYRQQ1ZXXbG1guvY\noeqUfAQFzwGA3jSlXLfBxZuZdcKplcbNR1aeMxx8IeQnu7Nn9HvXF839KOKii09xC0tLIS0F\nSn6rpbCYv7H47/FlFaVRskQ56XPdlH4TLix/ed1DIx6p00gClH0Y+iQQEmiXA1RgnT4LIYSQ\nK3AwDWrw/OcB5Q+ln3p1WFd+3NWbMXKix0LZ9rkcfdnBFhc0IeHAndvXusLSUmgEsLQUcgL7\nxs2HQ5mOr4SvYQiZpU6ydvu8t5/yN7UdcG98ne2TxhvMk4Ewc3zrshO+6oQ6egpCCCE3aEYr\n76NGzOdxaHUMSLlbxvUTNAx8pMs1YxormOxOXTYkhTENdE7oWcO+UvbmjMDV1qwOAOIV4/rK\nJ360auWBp3SnPjLmHjazBre3f5Na4/8VV54s130NAHlJrOW/2+fNV6FyGwgGdz8XIYRQrWFi\nhxqLW+9Vt+R2gxOGewfLtpe9I9gMm8s2XdhdsXqwaobr968L143pHWQJUkJuV97Fa9g1Yyqr\nE/KS2TNrTAee1qctM+YcYs2Vbs7wBOGO9e1uZ3hlX0DORLjSAgzp7n0iQgih2sKuWNT4WHI7\nS6MRASYBHHSqVk8qkX3x7vePzL//8s3kbvIRCtLvuin9mGbb8LDpg8PGmXXAGQTe8Y4VHsMB\nSxEOvlIKGNvuY94kFKSwBSksQYJfRyq4Jx3Si5QF1N2vcAJXtIUigGcpEjcrQwghT8PEDjVW\n6gS66MR5P8WISv0KJ/aW7dqxx8Et6V/v+Dzt/InMyrIOrTtuHrazf897bOvwJsGkFXgTwZsE\nVieYtAJvBt4kmDVgth5rwawReDNwZoHVCmYtcCZgdULtZ9DeRRgTfUSzlQeOhDsG0102Jocx\nHcX1BR5KznEl57iLX4EynAiJp4PjaO82bs/wiBLNXi9mswASbTIJwOIyeAgh5EG1mBV76NCh\nOgpi4MCBdXTnuoOzYj2P18D13mC6YMktTOwATwd0B7cnhUZBtzA7boDq8Yd8F1kLr5vSl9y8\nd3bQhm7ykTWJyiuYCI6j1Am0T3uKqOOBGLcyPN0hKPscfKaAYhhAM9iWFyGEPMrJvWIRJnYN\nAAdFb0PR26AclZe9w+n17RoagQdOL5h1AmcAzgisQWC1AmcSOD2RduXYC9seCiNiOnsNk5Pe\nV42pSdofR3rPGef3Wm2fIvMjg3qSwb1o/05UnU2nBQDwkc/wknwDANAqGWS96/BJCCGEMLFz\nGiZ2DYXmN/DqC5SfuzYfa+DyinK/2fFF+oWTGm1l68CO/YMmhNxI0N50/l8xLScCO1NBPcjg\nXjQtc3tyLAQoBzB0Est1oGMyHJznSiHvaZC0BsUo607BCCGEnIaJnZMwsWuAmkluJ6bJ4gvT\nuIJUtuwS7/TYPpIBvygqKI5S96Glvu7M8BgqnSCKTewQ8Sl1t1NwvScAQNDbEPCqg4uNZ4C9\nCUxrkLTBnlyEELqrWgxz3rRp02OPPUbT9TEymmXZzZs3T5s2rR6ehZoM29myzYoyglRGkK1H\nM/oivuAEV5jKlZznBL52N+HNUHyWKz7LZWw2+bQng+Po4N60Qu2GDM/MdavqVOm5LD8FASCU\nZ0boL9/+xt2egVH2JZR+DADQNhOYlo7uwWHChxBCVrXbUqxNmzaLFi16/PHHGYapo4BMJtNX\nX3313nvvXb9+vSG3JmKLXUOWl8RK6AO8EMByXTwdi2eYK4XCdDbvGFd8huNdSHTrcjrtLQRh\noMgbPB/EC37is36KB6XMrwBMXnklCJSDKbeXA4BUguohCF5ZRxEihFAj4sxesZGRkS+//PL0\n6dMlklqvH1YNg8Gwfv36pUuXZmVlWUowsUNOMmfxV+II0FTqP9KZnvB0NJ7EGYXic1x+EleY\nwpl1zv+Dqs/ptLYk9BGaPE2Q5VrDS+KzJFEa7BMMAOAzHVo42kdYfwTKNgATCT6PAdOmjoNF\nCCHPc6ZfNTMzc9asWW+99db06dOnTZvWrl07F4PIyMjYtGnTpk2b8vLyXLwVQgAAZZ+RRBEA\nUNRFT4fiYZSUCI6jg+NogYPyK1xeEpuXzBlLa53h6QuEzD/YzD9YiYoI7EaFJFBBXWii7sdl\nmNhEEyRWeZrQG8xjKfK6Ia+TNtNBy6S6w3Eo3wAAIB/oOLHT/Q0EDUxroFu4K2aEEPKgWrTY\nxcbGnjt3Tlw+YMCA6dOnjx8/XqlU1urZlZWVP/zww4YNG44cOSI+26lTJ4ePayCwxa5hE6D0\nY6j8CSL+ykvGffPuIPBQeZ0vSGXzjrINeDqte6i8FiikHwFAYcUVjo+0lt/u0r3WDYyngImE\nttcd3UBoMsvoIISaiVokdmazeeXKlW+99ZZWqxWfZRime/fuffv2TUxMjI+Pb9GihXiaBcuy\nubm5SUlJR48ePXr0aFpaGss6+CVboVAsXrz4ueeeq7uRfK7DxK4x4C07zDbD6RQ11MCn07oF\nSRRTZKaZ7wqCgzkWIT6BBFFuYgeWaPY6GMBnPAVZI8H3afCbA1RQfYSLEEKuqfVyJ1lZWc8+\n++yOHTvuWlOlUvn7+/v5+QmCUFpaWlpaWllZederHnzwwVWrVkVERNQqqvqHiV3jgrld9VyZ\nTmtFkGCZThsST8tDGlyG54ggk/xIEZm8EKI3TRGfljG7fBXjAQACXoag9+o7OoQQqj0n17H7\n/fffFy5cePbsWTeGEhsbu3Tp0nvvvdeN96w7mNg1Rpb0jgCTAO6c99OUNKLptPVASv/lo3iM\nIDSFFZeDeztcbAUhhBoWlxYo3rNnz4oVK/78808XgxgxYsT8+fOHDx/u4n3qEyZ2jVTxiaO+\nionlui8drpeLrNw4nVYZqzvBbb9Slq4z6KLbxIweMiEyrLUbQ61TBGgZOsn6bnHQXYsQQg2J\nG3aeOHfu3MqVK3fu3FlcXFyrCwMCAsaOHTtv3rzY2FgXY6h/mNg1SlwRXI8DcxYAXVR5nOU6\nezqgRoBnofgMV3CCLUjhTBW1/rjIMp1bWfCwjFB2lPWXkcqrxtR/2eMvjV41pu/jJA20nCAp\noGUEKSFIBhg5AAWMvKF3495K77hCqNgKPjOArN28MYQQqjtu21KM5/n09PS9e/fu3bv36NGj\nDidYAIBSqezbt+/QoUOHDBnSrVs3kmys3TSY2DVKggHy50HZZ+DzeF7mek9HY48ir5FEKS/4\ncbyDBi2GPuklWU8SlVrDM2Yuvv7Dc2I6rUkwvJzTq7v83kf83yP/2x8iRffr2sInXlHvbi2N\nq+ZakgFSQlASYBRgSfsohiAZoCQEaVtoyQiVt19SDJAMQTJAK4GWEJSMIJzamaKgOO/Cv2cN\nRn10m1iHTYzqdu9A0ZtA+UH4r+DV15lnIISQu9XVXrGVlZUFBQWFhYWFhYUAEBwcHBQUFBQU\npFKp6uJx9Q8Tu0as8idQjARS7t4ZFSSRT1NnSaLSzPXk+HBxBYV0hZTZA6Av0Rx0uIiG2kcG\nBGcwjy3T/iA+K2V+9lOMA4By3Sa96VFxBQK0Aihc/TJqpjKTzz/BFp7kKjKrm2qRpP3hh9I3\nPwhLo4g7ZrivL5rLgfmpwM/qOMxbKClB0CBRApBAexEkQ1AM0HKCoICWA0UDKSVoGUFQwCgI\noMBEat7/ad5vx7cqyUAp4VXE3kjo3n/Va+sjWrSyuSsf5N2eIm/wgh8ZdQMb7RBCDURdjRdR\nqVQqlapt27Z1dH+EnKd6yPKnOoG2ze0YKpmhzgBh0Bn/Z1knxU6AKpEkio3meyr0a8RnGfq4\nn+IhACjXbtTzj4kr0NQ5Cb0PAAjQhyR4Owjskhz4SpmvXt3Z0T9MrQqyAAB82hh8fO+oYPkq\nfORPMkyyyXxPuW5d3f3TtlBFkqpISbvxd5lOm2k6EyVLtMvqACDGa9DPZcvqNEJbnFEAI7C3\nehHu8qusAMKK/EkVXNHboUfDmGgAqOAKvrn04tgnhv711Un/IN//KpLFmiS5ZJ0Acu1xGQCL\nw+8QQg0BfhKhZs02t5NLP/eSfA0AetM0QXDQAEOTFwmiQu7fVh5RbeLV1miXeN2S7w0VfkB4\nhfSqoqXQfwEQDDCtHJ+VD4b2JUCqQLThgzqBBuDh8kHgihj6RH3+u/YKJCNHkZGjmCqm0zpe\n4JcA8q4Jlqec1x+8Yjz+QViaigq0lHhTwbOD1r+Ze8/iqR9N6b0wOI4O7kUrWhA8H6QxvGa9\n0PJGup3ecaVAKkGU1CKEUJ3CxA41d7d/EueroBQAIKSnCShH/zQyO4JgBKaKZS+ksaD+DEgl\nyHo5rhDyCYR8Ul0oga9Vd5aggfKr8iyvAdVY0O6jlUPVnWgQLd1HEjdp6qKZ6yMIsuqe4ixG\nRYT2Z0L7M7bTaSM0MSe1S3jgrAPsLC4a/omQNNApU+cMB7t4DbNmdRYkUH2VE9N0v5VlLCjL\nMF3aapKHEEHdqaAetH9HynYM3+30rvAl0PwO/v8Hfv8HhLSevwqEULOFiR1C//GdCcqxQEiB\n9HVcITKpustpNfg+VRdx1QjpDeovAAAEk6XAtmcwL4mVSXZ4ez0rCLJS7S8mdlDdBWLdnZZn\noe3J8dtfeXN76Tvj/RYT/zXdndXvP6zZslC9q+5icIWOL1eSAeJyFRmg5W+PqdXl/7d5rjcR\nHEcFxVEBnSnqv63VCo7nBqm+IQg9lK4F//n1FDpCCGFih9Bt0q7QBBpWCAdrL6sTaMjeDxog\nCCPLx9RPICQNkQk+33yy/fEXxp7LPRDjNdiLVP1rPHHGsG9GzLu9wxM5k8AZBIEDsxYEDlh9\ng+icDaDDMwxHxeV57JUA2sGOOKYKIfsgm32QJRnwj6GC4+igOFIeBHrTk16SjZUVz+qSBHVC\n3ceNEEIAUHezYps8nBWLGhnDcdD8AewNUH9pLbPprhW8veaYuQQTO4Tjw9z75JKyom2/f3Pq\nYoreoItq3enBEZOj21SZXPImgTcDaxJYDfBm4M0CZwbOJLBagTcRnEngzWD5P28SzFrgTbde\ncmaB1VrOAmcCTi84tzdarjnjtdx+L6t/bSe9vaZMOZf/Wm6/SX7v9FVOrMlNlOFEUBwd0kPn\n014KhJelEGdXIITqASZ2TsLEDjUpxtNwrSsA6Iz/q9Cv8nQ07sEaBIG91RzIGQTOJPAssFqB\n54DTA2cG3iTcqqMTBBY4w60cccu5JbsyPxnt82Inr4FSQn7FeHxX2dIwScdng78lHE2XrobM\njwzsTgb1oAJjKVJCgDW9K3gOJDHgMwWH3yGE3At/g0QIAeiTLX/K2wytOOfZUNyGlhEAwNya\n31yL3Sx6wpsD/+q0buvKn668zbFsuF/bUW1mDDTNqW1WBwCGUj57P5+9nyUlhF8HMiiOMpbw\niqDLgaqPAXjQ7ITwX2t7T4QQqga22DkJW+xQU2O+Abp9oHwQqNtzR6x9tb7yx0kyx8QO0Rhe\nrlWS1KixHMtxrFQiAwBTpVCUzham8IXpHGd06WPTO6KiZcJXEfG/BQ97HrzHuylYhBACwMTO\naZjYoeaEh8shwBWxXGxRZZqng/Ew3iSUXuILU9i847yxxKlxfP+ReENgVzqoBxU7U0o3+B1y\nEUKNQjPtihUE4dSpU/v377969WpxcTHHcYGBgWFhYYMGDYqPj6fpZvrXgpBjXBFIO4P+GB00\nVB3jcJG8YoKo5PhWngmvfpESIiCWCoiloqeCJosvTOMKUtmyS7wTKy6bKiD3MJt7mD2zxhQQ\nQ7abIAm/h5aHkKD9ExTDHG5/ghBC1WuOLXaVlZXLly9PTU11eDYyMnLRokUtWrSo/ibYYoea\nHUEPvA4o+zXe8pJYufRTb695HNemTLfVzMV5JLrqEaAjCD0vOFigjgCd0usdArQmLsFgmiyu\nQFMX5dJVHNfBaL6f5ds7vL+hmC9K5wtS2Ds33qh9nCT4d9SFd/s4IvFsQP85oBzp/L0QQs1S\ns0vsWJZ98cUXr1y5Uk0dHx+fTz75xNe3ilVqAQATO4RsZY8Bzc8AZEF5rsPkyQ0IjhCMAsjF\nZ2jqtIzZQRBavXE6y3cUV/BT3iul/xIE7/zyYgc3Bm2Iry8A6E2Pl+vWW8utq5OUp2zyUUwH\ngDLdVoPJwZA4AnTWwCwbbxSmcoWpnLHUpU9XZag5dKAifAgd2p+hHKxOiBBCDjS7PsetW7fa\nZXV+fn4ymSwvL8+a45aXl69evfrVV1/1RIAINUI+U4DyBa4kODrEUnBnX62glL1vMvc3cfEA\nDvZOlTE7KSqDIPQa/RviswShD/YOJgiD3jSlXLdBXIGhzipl7wCAmU20TexurxuX4w2VQBAa\ndQLlaOaHN1wkAXivAK1XFwcfiT6R16EIAMA3phNIHfRE+yrHM9QJlutSotln3XhD4KHyOl+Q\nyhamcRXXnOmo1eQyl7aaLm010V6Eug8VMZSJGMp4BeFQPIRQdZpXYmcwGH7//Xfry8DAwNdf\nfz0yMhIAysvL33777UuXLllOHT9+PCcnJyzMzSu1ItQ0qcaD6o6mLNvFeItPpChli0EG5bov\n9aap4qtlkm9lzA4QKI3+dbvES51AAyjhohkAqkq8QOMD2QAAvu314O2ognwQkAogFCBwQIgr\nEND2ChAyIFWOv7rAN8D3KTBlgCRK/NUBAPx7GcxlJFF6x01J8G5DereRdJhQypf9nJfWPfdE\nZNFpWqh9Ry2rF7L3s9n72aRX9f4xVPg9dMQQJqAzdfcrEULNj9sSu7S0tL/++uv06dPFxcUG\ng6FW1x44cMBdYVTvxIkTWq3W+vKRRx6xZHUA4OPjM3PmzAULFljPHjx48NFHH62fwBBqwgLa\nHoQCAACfdlIfh4nXTW8oByA4dW8TkArRaQJU4wAA5ImOHyAfDK3PASkHuoqhsX5z7xIi0/ou\nFehQoEOrOCeAYhiYLtKSaMvMElt5SSxNnvOPfFIdCW1HLKkof774PFeYyhWmcMayWjfiCTwU\nn+GKz3CnVhmVEURoPyZ8CB02gCEdNIMihJopNyR2ly5dmjlz5t9//+36rerauXN3LL3avXt3\n25cdOnRQKpUajcby8vz58/UXGUJNmN9ckMWBdj/QLR1XCFwMfs8A5Qekl+MKYd9Xd3/SG6Sd\nXA3SeQSoP6/qnDqBhrJ/IQ8AQNW+o0rJUDIiOI4WZth01F51Zs0UTZZwq6NWTqgTqIihTMQw\nRpDrL1y4YDabO3Xq5O3t7fSXhBBqvFxN7FJSUgYNGmRNhhq4zMxM67FarQ4IuGOUN0EQMTEx\nycm3luC/du1avQaHUFNFSEE+GOSDq6zAtHE09K6pUI0BpiWYLoGsF9h047boCx0ekQCAJnlS\n7lGvrKT7c1KHOtNRqxOy97NX92m2zV58sHITIZAUQZtA9/DDD3/yySeBgYFu/WIQQg2dS4md\nwWB46KGHGktWBwCVlZXWYz8/P3EFHx8f67FWqxUEgSBwqDJCyAVUICiGgWJYVeeVYfoO9+/p\n8OAZk99DOYfYrL3mnEOsqaJ2HbVrCqcVmq8vDPm5rbQXAWSO6cLmnQuGGMDAlwAAIABJREFU\nXRyWlJQkleJ2tAg1Iy4tgLlp06YbN264K5R6YJvYeXk56PSRy28vpiAIgu2APIQQqhPhu6B9\nCbQ8JPEhWo9mBnwsn5TqPeoHRewsqU+7Gn1En9HvyzAcfVG9q700gQSKACJc0ml+yPbcs+Xv\nz/1M4Or6C0AINSAutdjt2rXLXXHUj1oldgCg0WiUSqX1ZV5e3p9//mk5Pn/+PEmSS5cutbvD\n0KFDe/ToYVd4/Phx8QSRjh07jh492q4wJydn8+bNdoXe3t6zZ88WRyt+OgD873//U6nsJ/d9\n8803ubm5doVjxoyJjo62K9y/f/+JEyfsCnv27DlkyBC7woyMjJ07d9oVtmjR4vHHH7cr1Gg0\nn376qTjUBQsWkKT9z61169aVl5fbFT7yyCMRERF2hb/88ot4HOTAgQMTEhLsClNTU//66y+7\nwnbt2o0bN86uMD8/f9OmTXaFcrn8mWeeEce/fPlylrXvOXvqqafEjcHfffed7TAAi/vuuy82\nNtau8PDhw0ePHrUr7Nat24gRI+wKr169+sMPP9gVBgUFzZgxw67QaDR+9NFH4vife+45icR+\nebT169cXFRXZFT788MOtW9tPL9i9e/fp06ftChMTE/v162dXePr06d27d9sVtmrVauLEiXaF\nxcXFX375pV0hwzDz588Xx79q1SrxPK3p06cHBwfbFf7444///vuvXeHw4cPtRtkCwLFjx8TD\nhWNiYu6//367whs3bmzdutWu0NfX9+mnn7Yr5Dhu2bJl4vjnzp2rUNjMFCEVAPDVV1/l5eXd\nLhwKPgOEHpL2pSmt8tPa8qzjDoQz+r+6y0f5UCG2hRJC1lc5cceW3ztefjSv7d/6dpeBvN0K\nGBYW9thjj9ndp6KiYu3ateL7L1y4UFy4Zs0a209UiylTpoSG2s8y2bVr18WLF+0K77nnnl69\netkVnjhxYv/+/XaFUVFRY8eOtSvMzc395ptv7AqVSuWcOXPEoX7wwQfiFVtnzZpl20VjsWXL\nluzsbLvCBx54oFMn+0GcBw8etI7bsYqLixs2zL5p9vLlyz/99JNdYUhIyLRp0+wKdTrdJ598\nIo7/+eefF++H9Pnnn5eWltoVTpo0yToj0Oq33347e/asXWH//v379u1rV5ienm79GWfVpk2b\nCRMm2BUWFhZu2GC/9pBUKp03b544/pUrV5pMJrvCJ554QjxOYNu2beJBUKNGjerSpYtd4ZEj\nR/755x+7wi5duowaNcqu8Pr1699/bz9INyAg4Mknn7QrNJvNK1asEMf/7LPPymQyu8KNGzcW\nFBTYFY4fP75t27Z2hXv27ElLs9+JsU+fPgMGDLArPHfu3K+//mpX2LJly8mTHSycXh3BBeLt\nGXr27Ll169a0tLTc3Ny8GnMlhpozmUwP2Fi+fLm4zo4dO2zrXL582fbsyZMne9iwG6JnsWrV\nKvFtP/jgA3HNSZMmiWuK36mW76u4Js87HnCdlZUlrtynTx9xze+//15c8/nnnxfXnDdvnrjm\njz/+KK7Zu3dvcU1xTmlhNpvFlcUJBAAcOnRIXNPhnOUlS5aIa65evVpcc/To0eKaKSkp4ppB\nQUHimoIgiP+pA8ClS5fENe+55x5xzU2bNolrOlw98emnnxbX/O2338Q1O3fuLK4p/vS3qKio\nEFcW/wADgD/++ENcU/yxCACvv/66uOb69evFNYcNGyaueeHCBXFNhUIhrikIgr+/v7hyenq6\nuKY4LQOANWvWiGu+++674ppTpkwR13Q4l79t27bimkajUVwTABx+9PXs2VNc86effhIEwVDC\nX/nJdPB/2i2x5Ztaldn+11c58V6fZ+0KN7Uqmx7wcRtpD8vxu2FJ8YpxxH+9NImJieKni3/9\nAACCIBz+/YeHh4srHz16VFzz4YcfFtdctmyZuKbDH6vjx48X10xKShLXDA0NdRgqRTlYGuba\ntWvimv379xfX3LJli7imw2R37ty54priX4ABIC4uTlxTnChY6PV6ceX27R1sgrJv3z5xzalT\nHawx9Pbbb4trrlu3Tlzz3nvvFdcU/1IHAL6+vuKagiCI2xoA4Ny5c+Ka4t9gAeCLL74Q13zj\njTfENWfMmCGuuWfPHnHN6Ohocc2qxpUVFxeLK3ft2lVc85dffhHXdNgus2jRInHNr7/+Wlxz\n0KBB4po8z4sLrVxqsSsuvmMZ9xEjRuzevbvBDkpjGIamaWsTi8NPW7tCu/aMNm3avP/++5bj\nEydOfPDBB9u2bbO7Q7du3cS3HT16dKtWrewKxU1QABAVFSW+p107ogVBEOKaAOAw3XznnXfs\nvlkAIG7ZAoApU6bEx8fbFTr8BImPjxcH4PBnrZ+fn8NQHX7arl69WtwD3rGjg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X+BD45hu03jBC8ZSvmU5YZ/J8kyFbrVxZUH806NrJ9y\nAZoDscGO47itW7d6NvshPj5+y5YtcrlcZA0AANB4qe4lRW+qdUZF2yHS6DuEr1ZkWE99YiSe\niIjnFWZrZ8IkWYCaiQ12RBQaGnrgwIE5c+ZIJBIXH5FIJLNmzTpw4EBYWJj4AgAAoEmonlEh\neCnmUa5VL+Efkbxkc8ZPJseTyHYAQrwQ7IhILpe/++67Fy5ceO6559q3b1/Lne3atZs7d+75\n8+dXrFjBccJt7wAA0IxVZzupJDVQOYshbfVJhqW4pzl/tfDesun/Zyw4giQHUDdRs2JrkpOT\nk5ycnJeXV1paqtFoAgICQkJC1Gp1v379oqKivP51PoFZsQAAnuMNdKk/GY6bLTElVXus1qub\nUlZd4ZMW68xagR8mqYLp95pC1e669oir7X+8HtMpAKqJmhVbk7Zt244dO7Y+3gwAAM2B6SJZ\niojIwneyWq9tPuQfyfSaxR15S++w8wQRmfX80Xf0iW8o5aprrXp5SWZ1zEoqW0Pt/yZJsOMz\nAC2Pd7piAQAA3CDvSh2PU9BjXJe16kSZ/ZWwnpKbHhCeVKcr5I+9Z+DtumT9uRWU/ywZTlPu\nxHqtF6CpQLADAABfkIRR5BckbU1OE2Y7jJZFDRXuUCo9azn9hcF2qDU+ZrbE8ryCgh6t12IB\nmgoEOwAAaHS6TuVCugpPks3+03z5t6utdjwfWKrdXKL5M+/0/Q1YHUDj5eoYu7vvvtv+MCoq\n6pNPPiGiKVOmiC9i7dq14l8CAABNlzpRar+CCSuhuFlc0iK9vthptB3Rma8MfpFMWHcJEVks\nnSxEVD3erual8gBaCFdnxTLMdVPQu3TpcubMGefznqmPmbn1DbNiAQC8Li/JzJCWJ0V1h1Jl\npjX5Fb1FL/AbIQtgEpco/Vpf9xuEYAeArlgAAGgs1L2vhKqGqhQvVx+qotkeM+Qk1IBg0vAp\nb+tM1y+MglWLARDsAACgceBNlDVEJknxV7ylkG2rPte6n/SGsTLB26ty+eMfGBwWRkG2gxYO\nwQ4AABoHRkbqVUQSo3mQ0TLAdvrG++TqAcJ9rMUnLOkbjA4n85LMVLaGrkytx1IBGisEOwAA\naDT8R1K7X+Sx1/aiICJiqMeT8sBOwj9YGT+Zsv+4rpUuQPkK5U2n8i+p+M16LRagEXJ1nOml\nS5fsD2Wyqw3jSUlJ3i0IAABaNP87iEideF2nKitn4udxyYsM+lKhSbJrDQFRbHDnq8nPYLzH\nn1vBkAF7UUALVC97xbYEmBULAFDfHAbMlZ6zHn5dZxUaRMcFM4lLFIqwq9lOIdtq5UND+9zW\nAEUCNCroigUAgEbKYfmSkJvY2CeEdxszlPEpyw0Ww9WmCr1pjNF8CyZSQAuEYAcAAE1Gm8Gy\n6LuEJ8lWXrKe/MRI1/dCIdtBS+OFtRxfeOGF6g+BgYG2z7XLzs5euXJl9eewsLB58+aJLwMA\nAJofdaK0IPlyoN/sCu0KK68mopgH5dpca+FRi/PN+cnmi1vZTvdel/ywIwW0KF4YY2fbfCIq\nKury5cuuPJKWltalSxd3n2pUMMYOAKAhGM9S1jAy5xjNA0s1v/EkJyKznk9+Sa+5LDCRghiK\ne4ZzTnLIdtBC+KYr1j4PaTQan9QAAABNgCyapGoiYsjEsOXV56QKJmGeQqYS2pKCp1OrjZVZ\njpkvL8lM5d9Q+bf1XC6Aj7n9N5idO3fWdEmv19dytZrFYikqKvr4449tZ6qqqtytAQAAWgpG\nSW03U8m7soi3rMnXfrOUEUzcM9yRN/W8U7OdxcAffceQ+IZCHngt+QUoXqYrS4nhSNaB/AY3\nTO0ADc/tYDdy5MiaLhUVFdVytSaBgYHuPgIAAC2IrD21/oCIiK6bCRHWXRLzCHf2a4PzE7oi\n69H39H0XK9l/f+WsfDgREW8m4xkEO2jGfD8rtl27dr4uAQAAmgDncXLRI6VRtwu3UJSlWU9/\nfi3zaQ1PVxnmlGq2UPAT9VgigK/5PtglJCT4ugQAAGganLNd1ylcaDeJ4M05e82ZO6418lXq\n3jKY78QCKNC8+TjYsSw7f/5839YAAABNiEO2YyXU61nOr7XQRAqitG8NzgujINtBM+bLYBcc\nHPztt9/GxMT4sAYAAGhy1IlSlilRyLZUH8pUTPw8pVQpkO14K534yKDJdlzYC9kOmiu3J084\nLya8fPny6g8BAQFPPvmkKy8JCwuLiYm5+eabIyIi3C0AAABaOmNaRPgYMqSXarcaTCOJKCCK\n6fFf+bH3Dc6TZM06/th7usQlSqnfdckPCxdDs+SbBYqbASxQDADgM+Xf0JVJRGS0DCqp/MN2\n+uIWU/pGo+ATYT0lvecrmOsH46kTpVS5heQxxHWrz3IBGo7vJ08AAAC4J+hRCplFAXeVarba\nn+40RtbmZuFGuOITlrT/OWa+iqPvU844yr6LLAX1VSpAw/JCK7RttWF/f3/xbwMAAKhbxHJi\nJK2jmOtGyzEUO52ryrOWXxDYbSzzV1NAWzbqNtsPn1Uu3UtkJVMmaXZS0CMNUTZAPfNCV2zL\nhK5YAIBGwmEmhKGUP7BYbygRyHashPosUoR0vdojyzC6kIBRWsMTwQlIddBM+L4rdunSpYsX\nL/Z1FQAA0FQ5zIHgQpiEuZyEE5gka7XQ0fcN2vyrLRo8ryyp3KM3PoBJstBseG1CUGlp6Z49\ne7Kzs11vxCotLT1w4MDBgwcnT57srTIAAAACO7Gx0+QnVgrsNmaq5I+/r+/3ikKiqE5+V/Mf\nJslC8+CFf4lLSkoWLFjw5ZdfWiyOi0ACAAA0AHWiNC/JzDAaIivPBxJR5CBpZZY14yeT880V\nmdYTq4y9nuUY3/daAXiZ2H+pS0pKhg4d+tlnnyHVAQCAD6l7XwkNuD3Y7wFirv4edZ4oj0gQ\nbr8oOGS+sNlxkiw6ZKEZEBvsZs+efeLECa+UAgAA4LncCTJJCifb5S9/v/oEw1KPmfKAKOFf\nugubTVf+cUxyV7Od9i+yVtVnrQD1RVSwO3/+/DfffOOtUgAAADwXuZbYIPK7RWd61HZOqmAS\n5nNyldBOsjyd+tRYftFx8mxFymq6PIyuPEIkMK8WoJETFew2btwovoLAwMABAwaIfw8AALRo\n8hiK/ova/W61XrdZpTKc7TWHY4S6ZK1G/thyg/3CKAyj8Ve8RbyJKrdQ1c76LhnA60QFu1On\nTtkfduzY8euvv96+ffvixYtZ9tqbX3/99b1793777bevv/56jx49bOflcvnOnTsLCwufeOIJ\nMWUAAAAQEXE9iZE6T24N6SLpNpUTfEJfaj22wmD9t0uW5wNKq36y8mEV2o/I/856LRagPoia\nFZudnW37zDDML7/80rVrVyIaOXKkXq9fvnx59aX09HTbSnWLFi3atGnTQw89ZDQajUbj9OnT\nDx48GB4eLqYMAAAAe9WTZO3PRN0qrbhoufybwPSIsnRr6hpDj/9eTX5mS5fCinSeVwU2RKUA\nXiaqxS4nJ8f2uUuXLtWprtrw4cNtn3/88Uez+er/LzEMM27cuA8//LD68NKlS3PmzBFTAwAA\ngDPndruuk7iw7hLBm3P3mS/9fG1hFJ5XESbJQtPktWCnVqvtL/Xu3dv2uaysLDU11f7qtGnT\n/Pz8qj+vW7fuyJEjYsoAAABw5pDtGAnFzeL8WgtNpCA6952x8KhQcP9iAAAgAElEQVTjul3I\ndtDkiAp2Esm1v/pUVFTYXwoLC+vQoYPtMCkpyeHBuLg42+G6devElAEAACCIZXNDA4bJpIeq\nD2UBTMJ8pcxPINvxVjrxoUFz2XEmLLIdNC2igl1g4LURCBcvXjQYrtu8xb7Rbvfu3Q7PWq3W\nWq4CAACIZc6NCB0gl+4N9hvPMnnV5/zbMD2fEd5zwqznU5brTZW8w/mr2c50oX6rBfAGUcGu\nc+fOts+lpaXvvfee/VX7YLdjx468vDzbYVFR0cmTJ22HWVlZYsoAAAAQIG1DfrcRkdXahphr\nrQmt4iSdJ8gFn9AV8Mc/NPBOWymVHfkfXexBpR/XW60A3iEq2PXt29f+cOHChXfeeedHH31U\n3S07ePBg2yWNRjN+/Pjjx49rtdpTp05NmDBBq9XarspkMjFlAAAACItcQ61elcX+ZbW2sT/d\n8T+yqFuF14UoPmVJ+/a6DigJmxnkP4V4HRXMJkOq4FMAjQTD845tzq5LTk5OTEx0Pn/s2LG4\nuDir1RoZGVlQUFDnewYOHPj33397XIZPbNu2bdKkSWVlZb4uBAAAXOIwWs5qokOv68rShbeX\n6DaVazfsWvJTyr8JUj5BEW9Q2PP1WyWAOKJa7Pr373/zzTfX+GqW/c9//uPKe7p16yamDAAA\nAHexMuo1R6EIE/4dPPuVoST1Wo+szvhokeZ4XvrchqoOwEOigh0RffHFF/ZTKBzMmDHDfguK\nmkyZMkVkGQAAALVzXtmOC2YS5nESTmCSrNVCxz4waPOvdWqZLTGESbLQ6IkNdjExMXv27ImM\njBS8mpCQ8MILL9T+hqlTpw4cOFBkGQAAAHVyznaqDmyPGXISWtvOVMmnvKMzaWuYJAvQKIkN\ndkTUu3fvc+fOvfbaa126dHG+umTJkiVLlkilAmNUGYaZPn366tWrxdcAAADgCnWilGWK/blr\nyzi07i/tNEZ4Dl9VDn/iIwPvNAwP2Q4aLVGTJ5zl5OSkp6fHx8cHBQXZn8/Ozt64cWNaWtrF\nixcLCgrCwsL69Onz4IMP9urVy4vf3pAweQIAoEkynKTsMWS6WKl7u8ow++pJno5/aKgprnUa\nIxNcHkWdKCXDCeJ6kGCLH4AveDnYtRwIdgAATZL+EGXeQrzeZIkrrjxAdLWtzmLgk1/SV2YJ\nTZJlqMdTXJubnYboSX8NUd1PQZNJ/QmyHTQSwqv4uOjzzz+3WK5bxrF79+61zJMFAADwMUVf\nivyMKjaW5HxlS3VEJOGY+Oe4pEV6Y4VTewdPqZ8a/NVs0A3Xxi8xTHmQ/2TiDVT2KanGkv+I\nhigeoC6iWuz8/f3t1xkmopdeeunVV18VXVUTgBY7AICmzrnvtSzNemiJzirUJcuFMAOWKLjQ\na9lOLv0nxH90lX5uQPzL9VongOtETZ5wni2hUqnEvBAAAKDBOE+SDY5hu03jBG82lPIpyw0W\nw7XWEKN5YFHFcY1hEeZSQOMhKtiNHDnS4UxmZqaYFwIAADQk52zXdog0eqTwOKWKDOupT4xk\n19Fl4dtVf0C2g0ZCVLCbP39+WFiY/ZmTJ0+KqwcAAKBBOWe7mEe48HiJ4M15yeaLP5qELyHb\nQSMgKtgFBQVt3brVvvt17969u3fvFl0VAABAg2KYSgl76epnlno+xflFCk90Pb/RWHBEOMMh\n24HPiV2geNCgQXv27OnZs6ftzJQpU7Zv3y7ytQAAAA1G3TszLGBwSMAolrk6JU7qzyQ8p5T6\nCWQ73konPjJWXhZaGKU622n/osIFRFhNDHxA1HInRPTuu+8S0UMPPWS1Wk+dOkVE2dnZo0aN\n6tevX/fu3Tt27KhUKut8ydy52FYZAAB8p/AlqSSViAKUL1RoP6k+5x/J9JrFHXlLb7/zRJ7p\n/D7NusvGVPZxGnhn/ENjp3Rqd6P9m+TSv/nMuxmmkqx6av0+1reDBiZ2gWKG8cK/sk1xkWQs\ndwIA0HxYKykzkSThBXnfWa3h9lcyfzGdXWes/pxctenzoqdilbd25hKJ6Jz+QJr1rw9e/OI/\nt4+33V+9BgrDVFLwdCxcDA1PbIsdAABAk8eqqN1vJImIaC91GCcXfZesMtua86c533Txs6L/\nTmu1MtF/XPWlu4Ke/Ufzf8+89lhclz7RbTtWnzSaB5ZWbeVk2ysvLVWrkeqgoYkdYwcAANAc\nSNsQI9zY0W0aF9JVsk+zrqtisC3VVRsYMKGzZOB3P6+1P2k031Kpe5OIwVwKaHgIdgAAANc4\nr37CSihuFneFztykSHS+/yZuwPGUGpf6QraDBoZgBwAAcB3nbMcFMSE3ySy8QEqzkkWTwVRk\nCk+SJWQ7aFhix9h99dVX3igDAACgEVEnVg+2M9t+KBN6JWw5sWNM8AKHO0/p9sT5jUh52zDg\ndc5+J1l7eUlmdaKUzNkkjarfuqHFEzsrtsXCrFgAgGbOnGtKG6s1TNUZpxBRQXHe4IndR8hm\n3RU0myGGiHjifypbvqPi42VtDwVKIoJuZPu9qGDlwhMmpJITrYLvoKCHKeI9TJWF+oNZsQAA\nAE6sVZSZKJNcDlQeN1u7m8x9I8LUXy774fFFE5Nyv+/MDSCidMOBckvBMxH/C5REEFH5eeuJ\nVcZeszjn2MaQMcT/XrIUUckKUvSmwIcb/g8ELQTG2AEAADhh/SnkWSIyW7taLerqczf3vvWf\njaefnvm0PJiI6HbVE8vaHumiuNn2UH6y+fwPRueX8SQv137D8yq9cTypJjZE/dBSoSvWQ+iK\nBQBo/sq/pMCH8pIlDqdNlXzSizptvtAPKEM9ZnBtBgt0iEklqWZrF+IlzpMzALxF7L9bL7zw\ngvgievfuPW7cuLrvAwAAaEhBjxGROtFxZqtMxSQ8p0x6SWfWOmU7nk6tMShbMSFdHeOg2RJb\n/eHqXAqAetAothSbPHny2rVr676vMUGLHQBAi+K8aknxCYvDTrI2MhWT+LrSr3VtP5HIdlAf\nMMYOAADAE2E9Jd2mcYKXTJV8yjtC7Xl2sL4d1AcEOwAAgLoJNrBFDZW2HyHc8FaVwx/7wMBb\nantnXpKZTBeo6HWvVAhACHYAAAAuUidKWaYoUDmLYXS2k10mceHxjsPpqhWfsJz+wlDLCyVs\npiX9dip6iQpmE2EuI3gBgh0AAIBrDKcjWg3041YFKp+0nWNY6vkMp2on/Hua/Yc5c2eNXa4S\n5jLLFhERGc4Sb/J2udASiR25+fPPP7t4p1arTU1N3b59+8GDB6vPBAQEfPjhhxEREVFR2GIF\nAAAaPUlodfySSU6wTKmVD6k+LVUw8c9xyS/qDeUCrW5p3xiU4RSRIPCDa7QMKtX86MetLM/9\nunU7eb3WDi1EQ69jZ7VaV6xYMXfu3OrDm266ae/evWq1uiFr8ArMigUAaIn0h6jkA1Kvzjuo\ncLhScdF68DW9xSDwqypVMP1eVaja19FLhnmyIF5Dd8WyLDtnzpwJEyZUH547d27q1KkNXAMA\nAICHFH2pzTpiA5yvBHZiu8+QC24Da9bzR98xCLbn2cM8WRDPN2PsZs+ebfv866+/7tq1yydl\nAAAAeEawdU3dX3rDWJng/boia8rbwu159pDtQCTfBLvY2Fj7lY3Xr1/vkzIAAAA8JpjtbrxP\nHjlIuEe14qL11CfGOie/Xs121nKx9UGL5Jtg57BfhW06BQAAQBMikO0Y6v4EF9xZ+Oc1L9l8\nfpOxztcWHMylzJupYHaddwI48E2wO3bsmP2kjdzcXJ+UAQAAIB7D6Biqsh2yMkp4TlHTfmIX\nNpuu7K+9v5UP8b+bDKlUsoJKP/RqpdD8+SDY6XQ6+zF2RGQyYfEeAABoktQJ58MCBgX6zbQ/\nKVMx8fOUMj+hbMfTqTWGsnShLWavYip17/F8gMmSQIGPeLlcaO7Ezqzeu3evi3fyPF9QUHD2\n7NnVq1dfuXLF/lJERITIMgAAAHzBSjn3SSWnpZITBvMIvXGi7UJAFBM3izvylp53inBWE6W8\no098XVlTq57RfHOJZqfZGsMfUqkT6694aIbEBrtbb71VfBH9+vUT/xIAAIAGx1LkWsocZDTd\nYjTd6nAtrKck5hHu7NcCu4qZKvmjy3X9X1VKBVv1iEyWq7+MeUlmrG8HrmsUW4rdd999vi4B\nAADAI4p+FL1f3v03Ky+w2H70SGn7EcKxTJPNH/vAwFvq/gasgQKu832wi4+PHz9+vK+rAAAA\n8JSiHxFTU7tal0lceLxE8FLxCUvatwLtec6Q7cBFPg527du337JlC8v6Pl8CAACIJJjtGJZ6\nPsOp2gn/0mXuNGftdCm05SWZyVqOebJQO58lKo7jpk2bdvTo0ejoaF/VAAAA0ACkCib+OU4e\nKDyc7uw3hsKjdffIMky56cwIyp+F9e2gFmLHYz755JNu3a9QKEJDQ3v06HHLLbeEhoaK/HYA\nAIBGRZ0oFew2VYaz8XMUh5borE4XeSud+NDQ7zVFTa161aRsmlSSSkRUtYus5cQGea1oaEYY\n+4WCW4iff/55zZo1dd72yiuvJCQk1HR127ZtkyZNKisr82ppAADQHBQezJRL9+qMDzqcz0sy\nH//QILirmDKcSVyirKlVr5pc+rdKObdUsyWifzsvVgvNSUsc3Hb58mVflwAAAM1X1fbwkD5B\nflPl0r8drqgTpZ3GyAQf0hXyx97VW2tdsN9ovrm48oCVj8RcCqhJSwx2WVlZvi4BAACaL0sZ\nWYqIzH7cCueLncfL29wsPA6q9Jz15Crh9jw7V5v0kO1AkDfXPCwoKMjJySkpKSkuLmYYJiws\nLCwsrG3btq1atfLit4iHFjsAAKhHgQ9Q1W5iA8oz3hS4ylDsdE5bYBXcVSwvyRzQjr1hrHCr\nnvPNWLsYHIj9F0Kv1//www+7d+/ev3//hQsXBO+56aabBg0aNGzYsPvuu08ul4v8RpHKy8sr\nKiqqP48ZM2bYsGE13YmNzgAAwEORnxMxrVsLt6uxMuo1R5G0WK8vFsh2538wKlszNbXqObia\n7XgjMT7+eYVGwvNgV1BQ8P7773/22WfFxcW133nu3Llz5859+eWXERERTz755DPPPBMWFubx\n94pk3w/bo0eP9u3b+6oSAABovmqbA0FEXDDTe4Hi4Ms6k9ap55Wn1E8NfhFscGeXhkvlJ1e1\nbjuR5DdSxPue1QrNiYdj7H744YfY2Nhly5bVmersFRQUvPbaa926ddu6datn3yuefT8sUh0A\nANSrWrpKA6KYnjM5Ruh32GqilHf02nyXlq0I8nuEND9TyQoqXuJxndBsuB3seJ6fMWPG+PHj\ni4qKPPvKgoKCe++9d+bMmZ49LpIt2CkUioiIiLS0tO+//37FihUffPDBd999d/bsWZ9UBQAA\nzVUt2a5VL0nMQ8JdqKZK/uhyofY8J1rDLJ4PsPDtKPABz6uE5sLtrthnn3129erV4r/4448/\nDggIePNNoYGl9cnWFatSqZYtW3bgwAH7q999911MTMzTTz+N/TAAAMBb/l21mHfuoo0eJavK\nt17eJTAUT5PNn/jQkPCcghHeafYqo/nmUs1PFr6t5Ui0OtGLVUOT5F6L3ZYtWz78sO5d6kJC\nQlwZRbds2bJdu3a5VYB4tmBXWFjokOqqpaWlPffcc6dPn3a+ZLFYKv6l0+kYpo4hFAAAANVk\nkiNhqr5SNt35UtdHuVY9hLNb0XFL2npjnS83WgZZrB0Ja6CAWztPWK3WuLi4U6dOOZxnWXbE\niBFjx47t2rVrmzZt2rRpo1AoiMhgMOTm5ubm5p45c2bz5s27du2yWBz3wuvTp8+hQ4fE/zFc\nVFlZ+dBDD7lyZ0hIyMqVKwMCAuxPHjlyZPr06bbDK1eu5OTkeLlEAABofnQHKGsI8SaTuU+J\nZi9Pjt2vZh2f/JJeky0wSZaIuk3h2o1wo4cNa6C0ZG4Euz///HPo0KEOJwcOHLh69eoePXrU\n+XhqauqMGTP27dvncP6ff/4ZMGCAizXUQqPR/Prrr4KX5HL5mDFjqmt44YUXbOd79uw5ZsyY\nmJgYnU534sSJr7/+ury83Hb1gQceeOCB68YrnDt3bsWKq6tNFhYW7t+//8qVK+IrBwCA5s5C\nWcNJ+4fO+GCFdhVP/s536Ar4pBd1xgqBH2WGpfi5ivCEWntkr4ds12K5EexeffXVV155xf7M\niBEjtm3bVt0+5wqDwTBmzJgdO3bYn1y6dKl92PJYXl7eE088IXgpMDBw3bp1RHT8+PFffvml\n+qRCoXj66aft19XLyMiYPXu21Xr1L0xt2rSpZTQh9ooFAAA3mHOo6ncKerSW3tKyNMuhJXqr\n0HWpkun/miIgyo0BVOpEhio2UKBL/VTQbLiR6B0a2wIDA7/66ivXUx0RcRz31VdfxcTE2DeM\n/fXXX14Jdq6Ii4uLi4ur6WrHjh379u2bnJxcfZibm1tRUREYGNgwtQEAQHMmbUtBj9Z+S3CM\nJPYJ+clVAoPqzDo+5W194hKlPNC14d2MRXdiqlK+nvSHsb5di+JG9nfYiWv8+PGRkZHufl/r\n1q3vv/9++zONaudWh5XtSktLfVUJAAA0S7V3krYZLOs0Rng/MV0hf/Q94fY8Z1L2jEK+hYio\n/FsyZ7tdJTRZbgQ7h27H2267zbOvvP322+0PS0pKPHtPfZBIrhvBwLIeLuAMAABQk9qzXef7\n5ZEDhW8oS7OeXGUgF4ZQmS3dSzW/Wvh2xWW/kjTKszqhKXKjK9Yh2HnQXCf4oLdaxdRq9Y8/\n/ljLDTqdbunSpbbDm2++eeTIkQ73ZGZm2h+GhIR4pTYAAAB7/65sJ4Sh7tPl2gJr+XmBSbJ5\nB8yqdmyne4Vb9ewZzTcXlZ/lSX51P1loGdz4X9povK7X38/Pz7Ov9Pe/bjaQwWDw7D3uUigU\np0+fNplM1YdarfaOO+6wX4suMzPz4MGDtsMOHTo4LHcCAADgRRL2PM+HWXnHRgRWzsTPVSQt\n1uuLBbJd+vdGZWumplY9e7Z1VZDtWo4W1NXIMEyvXr1sh+np6cuXL8/KyjKbzSUlJXv27Hnp\npZfsV9rzuK8ZAACgTurYLa1UiYF+0wWvcsFM7+c5qVJoqgRPp1YbytKFF72rCdYubiFaVn6/\n77777NdD3rdvn/O6etXUavWoUaMaqi4AAGhheD0VLmaYcoVsi0K2TW+6x/mWgHZs3DNcyjt6\n3inCWU107D194hKFIsyNBhq027UELajFjoi6des2ceLEOm9TqVQvvvii/RJ3AAAA3sQoqO0P\nxAZo9C/qzaNruqtVL8lNDwj/GBnK+CNvGUxaVxejrZaXZKKCOVS8xL1qoeloccn9wQcfDA4O\nXrt2bU1j+2JjY2fPnh0REdHAhQEAQMvC9aAbLgZIwjW1dpJ2GC3T5lsv7xa4R3PZeuIjQ8Jz\nCsblVppA5Vwq+YiIiPGj0Dlu1wyNnufBbv369fv37/fgQYf18BreqFGjbr755t9+++3o0aNZ\nWVkajUahUISGhsbGxg4aNKhnz56+LQ8AAFoKSTjVPkOWiIi6TOa0eXzxKcf91omo6Jgl7X/G\nLg+72sVkMI9QytcQI2W4GpfrhybNjS3F7CeQepfrNTQe2FIMAAC8qPZsZ9bxyS/rNZeFJ0x0\ne4xrN9zVlhqFbLOVVxvNAzHerllqWWPsAAAAmiKpkkmYp5CrhFtYznxlEGzPE6Q3jTWaBxLm\nyTZTCHYAAAC+V2f7mTKCiZ/LsUJ38VY69p6hpvY8aFEQ7AAAABoFdaJULt3tz71T0w3BMZLY\nx4WH05l1fMpyvbHS3UmyaLRrbhDsAAAAGofiN0MD7lIpF8ulu2u6pc0tsk73CO8npivgj75r\nsLoZ1a5lO+N5956ERsmNgZN9+vSpvzoAAABaOq4bEU9EcunfRvOwmu7qPEGuLeDzDggkuLI0\nS+pnxh4z3FuHNS/JrO78LhW9RG1/oIC73a0aGhU3gp39ng0AAADgZQH3UNhCUvTRpNa4ZDER\nEUM9npTrCqzlFwQG1eX+ZfJvw9TUqidILv2LChcQEeU+RDdcJEkr98qGxgRdsQAAAI1G+BJS\njalzIgUrZ+Ln1rifWPr/Ga/840aPrNF8i0a/mCc5RX6NVNfUIdgBAAA0OnVmOy6ESZjHSTih\nBVB4OvWpsfy8G5NkNfqXiytT8lLRD9vkIdgBAAA0SaoObNwsTnA/MauRP/qeXl/sRrYzW2II\n82SbPgQ7AACAxsiVnSHC4yWdHxCeKmEo5VPeMVgMbu/thGzXpCHYAQAANFLV2Y5hKmq5p+No\nWdTtwhGwMtN6/AMD7/66xch2TReCHQAAQKPF+3EfRQR2kkpO1nJT1ylcWHeJ4KXCo5Zz3xk9\n+OKr2a78GzKkevA4+AqCHQAAQGNVuTlQOYdhyoP9HmUYQ013sRKKm8X5RQrvJHvpZ9Pl3Z60\nwFWkrKYrUyjrNjKc9uBx8AkEOwAAgMZKNZYC7iZGoTU+xvNcLTfKApjezytlKuFsd/YrQ/Ep\ni5vfzctlvxFZyVJE+iNuPgs+g2AHAADQaDEU+QV1OBQYP7vOW/1aM/FzOFZouJ3VQsdWGKqu\nuDWRgimrWqc3jSnXfkZBj7jzIPiSGztPAAAAQEOThJMk3MV7Q7pIuk3jTq2+rtP2vCF5b+U3\nl3NTZY9K+w2JnzLhydjOPV17n6ys6nsi0iWZXZmiC40BWuwAAACaABejVdsh0o6jr+0ntrNi\n1bK8/7CM5LbAqYO4Ry7+WT5q6sDvt3/r7rdjnmxTgQAOAADQNKgTpa4ErM4PyKvy+ILD5kvG\nY9+XvjK39aauisHVl4aoJh2qumvem0/06zkoum1Ht749D+12TQFa7AAAAJoMV6IVw1LcTHnQ\nDey+yvV9/P5jS3XV+vrf01k64Icd6zz4drtY6f7ieNAgEOwAAACaGIVsM8sW1nIDK2fi53D5\nlN6J6+18tZO8z4nDHq5Ol5dkJs1PdGkAWUo8ewPUKwQ7AACApoPXqTv8N9h/QpDfVKLaZrly\noWzoDZyR1zlfMvG6qguy0nOetLpxsl8oeyzpD9LlO4mvcWk98BUvd5abTKY///zz0KFDKSkp\nubm5ZWVllZWVKpUqJCSkbdu2/fr1GzBgwIABA1gWgRIAAMAjuiQi4iS7ZNIkk3lALTf269N/\n25ldo4Pm2J+0kuW47rdhgU8cXa5PfF3p11p46buamMz9zZYYqSSVAkYTU9vSeuATDM+7vT2w\noLy8vGXLlq1fv76oqKj2Ozt06DB16tRZs2apVCqvfLVPbNu2bdKkSWVlZb4uBAAAWhjDacoe\nTerVeSdvq/3GotKCwRN7DGam3BuykCUJEZl54/qS54/rdi1tc1DB+vu3YRJfU0r93ct2LJPP\nyXbojJMwl6IR8k6w++STTxYsWFBRUdsuxQ6ioqJWrVp19913i/92n0CwAwAA37EQSVyZIXvk\nVPK0hRNMpcwNXF8rb0k3JCtY/5nh66PkXatvCOki6bNIIbissSuQ7RobLwS7+fPnv/POO558\nN8N88skn06dPF1mATyDYAQCAz7mS7XR67W/7f/39myPGQml7eY+eyuFSRm5/Q9sh0u5Pet6p\nimzXqIgNdmvWrBGZzH766afRo0eLeYNPINgBAEBj4OLSwWYdf/BlfeVl4QkTnSfIO42RCV5y\nBbJd4yFqEkN+fv68efNqvycgIIBhauu8nz59ult9uAAAAOAuqZKJf47jgoR/kdM3Gq/s93xv\niavh0nCSeGxQ4WOigt369esrKysdTioUikmTJu3YsSMtLU2j0VRWVmq12nPnzu3cuXPy5Mkc\n59jYm5ubu3HjRjFlAAAAtFiut5Ypw9mE+QqJQijb8XRqjcGzBVCqlRzeQ5kDKfdBZDvfEhXs\nNm/e7HDmsccey8nJ+eqrr+64446bbrrJ39+fiBQKRefOnUeMGLF27drLly8/+OCDDk8h2AEA\nAHhMncgEKF5TKefWeWdgJ7bHk3JG6MffaqKjy/VVeZ4M0GIYfZD/ZLJqqPJ7qvzegzeAt4gK\ndhkZGfaH999//xdffBEaGlrLI+Hh4evXr7/33nvtT6alpYkpAwAAoEXLmRigeN2f+4iT/Vjn\nva37SztPlAteMlXyR9/RmavcznY8ryjVbLXyYVWGORT4gLuPgxeJCnYOS9a98cYbLj741ltv\n2R/m5eWJKQMAAKBFC36ciOX5QMa1LVw73i1rN1y4A7cql09ZbrC635tqtvQsrjxcqXvLxckc\nUE9EBbuIiAjb5+Dg4BtvvNHFBzt37hwWFmY7DAkJEVMGAABAi+Y/gtSrmBuP6k1jXHyi6ySu\nVQ+J4KXSs5bTn3uyV5jFGlX9AdnOh0QFuy5dutg+6/V611dO4Xlep7u2e110dLSYMgAAAFq6\n4Okk6+j6RApGQnGzOVV74RiQs9d8cYtJTDnIdr4iKthNmTLF9lmv1+/evdvFB3///XetVms7\nHDPG1b9hAAAAQC1cz3Z1LIDyvagFUAjZzkdEBbvx48cPGHBt++Gnnnqqzo1iiai4uPipp56y\nHbZu3Xrq1KliygAAAAAPKFvV4wIoVJ3t9ClU+qGYl4BbRAU7mUy2efNmW4dsenp63759v/32\nW4NBuG/eaDSuX7++b9++586dqz4TEBDwv//9z36sHgAAAIjh1j4QgZ3YHjO8vwBKNZkkxZox\ngvJnUfHbHr8E3CJqS7Fz585duXKlqqpq4cKFx48ft50PDw+/9957O3bsGBUVFRERUVhYmJ2d\nnZGRsWXLloKCAtttKpXqnXfesR+oJ2jIkCEeV1h/sKUYAAA0ZnlJZqkk1WyJdeXmjJ9M5/5n\nFLzk34ZJfE0p9a9tE6maKGSbg/0fIjJT0CMU+Y0HbwB3iQp2jz/++Oeff+7FagSJ3M22niDY\nAQBA42WtoLzpfPmWEs0+kyXelSdOf2m4/JvwqLiQLpI+ixSsR/vBKuTfyaV7K7Sr1InCi+eB\nd4nqigUAAIDGqHIzVWxgGEOQ32Pk2uJ2XSdxYT29vAAKEVIp8ukAACAASURBVOmND1RoVyNv\nNBj8gwYAAGh2giZT4ESS31SuXevibz0joV5YAKXpQ7ADAABojtSrqcPhsL59XH9CqmB6P89x\nocLZAAugNAkIdgAAAM0RG0Ssyt2HuFA2YS5XvwugXH2bqPY/qIlHIyH/NWTIEKlU1BsAAACg\nXqkTpW41lQV2YuOe4Y4u1/NOEa56AZT+ryn91Z5Mkq2Wl2RWJ1yky3dS6w8p4C6P3wOCRM2K\nbckwKxYAAJoQd7tBM342nVsvvACKX2sm8XWlTOVhtmPZglaq3iyTR4ySov8ihRudxVAndMUC\nAAA0f26tWkxEHUfL2o0QfkSbzx99z2D1dLyc1RqhM0wmIvK7lbjuHr4FaoBgBwAA0CIwjF6l\nnCuRXHTx/q6TuPD4GhdAOfWJgTzt86vUv16u/SI/5wdiFB6+Amrg/RFyOTk5Bw4cOHz48OnT\np0tLS8vLy/V6vW0PMZ1Ot3bt2okTJ4aGhnr9qwEAAECYpbB165GkT5FL/yrR7OP5uhMVw1LP\nZ7iDL+srswQmTFz5xxwQxXa6V+ZZOTrjo1Q93s7NpkSonTfH2G3atGnNmjW7d++2Wh3/DbB9\nS3l5eXBwsJ+f38KFC+fPny+TefgvhM9hjB0AADQpFsoaTto/rHxkiWaX2VLHfp42hhLrgRcN\nhhKhybAM9fwvFzlIbDJDtvMi73TFpqenDx06dNy4cbt27XJOdc60Wu3ixYtHjhxZUVHhlQIA\nAACgVhJq8x0FTmA7p7ie6qiuBVBOfmooOW0RWRnWt/MiLwS7lJSU/v37//nnn+4+uGfPngkT\nJrgSBAEAAEAsaWtqs4Gkanefq14AhRGKDLyZjq0wVOWJ7f27mu3MV8h4TuSrWjixwe7SpUvD\nhw8vLS317PEdO3asWrVKZA0AAADgOg+6PsPjJTc9IBe8ZKrkU5bpTJVis11B8mW6PIyybiPT\nBZGvasnEBrtnn322pKREzBuWLFliMHi4tTAAAAB4wINs16HeFkCpFqB4gwynyZxDBQtFvahl\nExXsjhw5sm3bNoeTvXv3fv755zds2KBWCzT2qlSqVatWhYSE2M7k5+dv375dTBkAAADgLg+y\nXf0tgEJElbr3DOZRRssgivzc87e0eKKC3aZNm+wPIyIiNm3adPjw4WXLlk2YMEGhEJhKzbLs\njBkzNm7caH9yx44dYsoAAAAAD6gTpcRYJOwlF++vXgBF1V44PFz5x3xhi+c7wPIkL6vaUFr5\nS95BpccvAVHB7vfff7d9lkgkGzduHDt2rCsPDhs2rE+fa1uIXLp0SUwZAAAA4AlLYYj/3aEB\nQ1m20MUnpAqm9/McFyqcH87/YLyy3/MeWZ5X8uRHmCcrgqhgl5OTY/t8++23DxkyxPVnBw0a\nZPucmZkppgwAAADwRPGbnPQ3CZsdqJzp+kN1L4CSKnYBFEK285SoYFdYeC3gx8fHu/VsWFiY\n7XNWVpaYMgAAAMAT4UtJEU+KXpX6pW49F9iJjZtZ8wIoH3hhARSyz3b6w8TrxL+wJRAV7AIC\nAmyf8/Pz3XrWPszJ5cIzqAEAAKAeMQqK+pGiD4T3vcndR8MT6lgAxSh6ARQiyksyFxy8QpdH\nUkZPqvq97gdaPFHBLiIiwvb5jz/+0Gq1Lj5oNpv//vtvwfcAAABAw5FGEVP3vrGCal8A5Zjo\nBVCqBXCvkKWYjOdJ+6cXXtfciQp2vXv3tn3OzMx85JFHXFyRbvHixadPn7YdxsbGiikDAAAA\nRPJsw9Z6XQClmka/RGucZrZ2zr/4vNh3tQCigt3o0aPtDzdv3ty5c+eVK1emp6fzvMD/kmVl\nZVu3bk1MTHzrrbfsz995551iygAAAADxPMh29boASjUrH1Kh/aS4MpnnFZhRUSdGMIG5SKfT\nxcTEXL582flSYGCgTqczma7+z9mrV6+MjIzy8nLnO4ODgy9cuBAaGupxGT6xbdu2SZMmlZWV\n+boQAAAAb/IgPBlKrAdeNBhKhDZ/Z6jHDK7NYE+aA2vhWftiSyCqxU6pVL799tuClyoqKmyp\njoiOHTsmmOqIaNGiRU0u1QEAADRXDFMR5PeYVHLO9Ue4UDZhXo0LoJxa450FUOzlJZmvBtDS\nj0l/xLsvb9LE7hU7ceLEhQs939PtvvvumzNnjsgaAAAAwDvMua3DE5Xyb4P97meoyvXnAjvW\nsQCK9ooXJsk6KD50iApm06X+VPyG11/eRIkNdkT0xhtvvPnmmx4sWTJ58uT169ezrBdqAAAA\nAC+QqknWiYgYRs+yeW49WvsCKEfe8s4CKPYUsv8j3kxkIdkN3n1z0+WdULVgwYIjR47cc889\nEonw1BgHvXr12rZt29q1azmO80oBAAAA4A0stVlHwU9KuhyxWN1OSx1Gy9rX/wIoNpX6ZWXa\n73TGSXmnx3nzvU2ZqMkTznJzczdt2pScnHzo0KHs7GzbynZSqTQ0NDQuLq5///6jR4/u37+/\nF7/UJzB5AgAAmjfPpqDyVjq6XF94VHhQXeRAac+nORIajOcVmFTh5WDnwGQyVVRUcBxnv0dF\n84BgBwAAzZ5n2c6s5w+9oq/IFJokS3TjePkNY2Xi6qpNC8929Tu+TSaThYWFNb9UBwAA0BJ4\nFpKkCiZhPseFCmeM8z8Yc/fV43J01ybM5txPRa8Qb6y/72qEvJ9qc3JyDhw4cPjw4dOnT5eW\nlpaXl+v1+nPnrs6a1ul0a9eunThxIpY4AQAAaPzUiVIP2u2qF0A5+JreonfqGOTp1BqDIpQJ\njXVpXL5nylLWBft9T5VEposU+U39fVFj482u2E2bNq1Zs2b37t1Wq2Prq+1bysvLg4OD/fz8\nFi5cOH/+fJmsHhtj6xW6YgEAoOXIT9ZImAyztatbTxWmWI6+q+eFumRlKibxVaVfZH2NtlPK\n1gX6PcswVdThECl61dO3NELe6YpNT08fOnTouHHjdu3a5ZzqnGm12sWLF48cObKiosIrBQAA\nAEB9MWWFBtweqhrOsrluPReeIIl5sEEXQLHRmR4urDxRrv0i71j3FrURmReCXUpKSv/+/f/8\n8093H9yzZ8+ECRNcCYIAAADgM6UrZJKDLJMfqJjr7qPRdzXoAij2rNY2OuOD1Z9bTrYTG+wu\nXbo0fPjw0tJSzx7fsWPHqlWrRNYAAAAA9Sj8TVL0Ib9bKnTvefB0l0lcRIJwtis9azm5ykD1\nuD7HNdcmVRCROYca5lsbnNhg9+yzz5aUlIh5w5IlSwwGg8gyAAAAoL4wHLX7ldr9HtG/nSdP\ns9RjpjwwWjhy5B0wX9hsErxUH/KSzMQbKGsYZQ4mw+kG+94GIyrYHTlyZNu2bQ4ne/fu/fzz\nz2/YsEGtVjs/olKpVq1aFRISYjuTn5+/fft2MWUAAABA/ZKEE+P5ShpSBRP/fM0LoGyq3wVQ\nHGiOv0rGs6T7m0rebrAvbTCigt2mTZvsDyMiIjZt2nT48OFly5ZNmDBBoVAIfB/LzpgxY+PG\njfYnd+zYIaYMAAAAaBgeL/+rCGET5nEShdA0WJ5OrTGUpApvVuF1OtNko/l2Kx9WcHlpw3xj\nQxIV7H7//XfbZ4lEsnHjxrFjx7ry4LBhw/r06WM7vHTpkpgyAAAAoMF4nO0CO7JxMzlGKHrw\nZjq2wqC90hDj3iyWTiWa7cWaf6zWiOY3qUJUsMvJybF9vv3224cMGeL6s4MGDbJ9zszMFFMG\nAAAANCSPs11tC6Bo6ncBlOsxFkun6k/XTapo+kQFu8LCQtvn+Ph4t54NCwuzfc7KyhJTBgAA\nADQwli0M8R8rkxxz98HaF0A5+m49LoBSi2vZTvMrmbN9UIGXiAp29pvA5ufnu/WsfZiTy4XD\nOwAAADRGpssRoX042U9B/g8yTKW7T9eyAEpZWsMtgOIgL8lcePAS5T5AF7tR2RofVOANooJd\nRESE7fMff/yh1WpdfNBsNv/999+C7wEAAIDGThZFir5ExPP+LFPu7tONagEUe37cCrJWkLWS\nLB4u0OtzooJd7969bZ8zMzMfeeQRF1ekW7x48enT1xaPiY2NFVMGAAAANCyGIr+isIWybgcs\n1igPnm9UC6DYVOqWVejeM5kT89Kfafhv9wpRwW706NH2h5s3b+7cufPKlSvT09N5XqAVtays\nbOvWrYmJiW+99Zb9+TvvvFNMGQAAANDQJMEU/gYxinpaACX104ZbAMWOVGuYWazZSyRropMq\nGMEE5iKdThcTE3P58mXnS4GBgTqdzmS62pTaq1evjIyM8nKB1trg4OALFy6EhoZ6XIZPbNu2\nbdKkSWVlZb4uBAAAwMfEBKDCFMvRd/W80L7xsgAm8TWlX6RQ8mtAHidXnxDVYqdUKt9+W3jV\n5oqKCluqI6Jjx44JpjoiWrRoUZNLdQAAAGAjJvqEJ0hiHnKcQ5mi/WVFwcRnz/Yc9GC36S88\ndOjkAXEFinKt6a5wMVV858NKXCF2r9iJEycuXLjQ48fvu+++OXPmiKwBAAAAfEtMtosedd0C\nKBtKFq8pmh4l6/ZA6NKxqsWaI6qx04ev2/q5N8r0XMnhvVT8JuU+SHmP+7aS2nmhdfGNN95Q\nqVQvv/yy0Wh068HJkyevXr2aZcWGSwAAAPA5daI0P7lEyp43WRLcfbbLo5y2gC86ZknV/bmn\n8osXI3e3k1+dWNnXf0xP5fBF700a3Pf26LYdvV21q6SSo0QskZUC7vFVDa7wTqhasGDBkSNH\n7rnnHolE4sr9vXr12rZt29q1azmO80oBAAAA4GPGM2GqQSEBd0rYS+4+ykgobhYXGM3+XfXd\noIAHbamuWi+/kTdI+m/9bYPXSnWf1vB0cWWSRr8479RIH5ZRJ6+NB+zevfvWrVtzc3M3bdqU\nnJx86NCh7Oxs28p2Uqk0NDQ0Li6uf//+o0eP7t+/v7e+FwAAABqFsi+l7BkiUileLNN+6+7T\nUgUT/xxXOOHiIO5h56vR8p5pZ895oUgRTJY4kyWO/p0s0jgnVXi5pjZt2sycOXPmzJnVhyaT\nqaKiguM4+z0qAAAAoBkKf5N0SSQJrcj50LMXKMLY0Gg/XZ7G+ZKe11jOys16Xiq4PIovXBfv\neD0xCh8XRETe6oqtiUwmCwsLQ6oDAABo/hgptfuZorZG9A/3+B0DEwelaH9yOGnk9Sd0uzpZ\nE8+s9c2OFLXISzIT8XT5Lsq5nyyFvi6nnoMdAAAAtCBsEJGoFrXHxv23SHH+u5KFZv7qXlZa\na/mnhdP82KC+fmNy/zLl7mt02a7i6GrS7qHK7ynvCV/X4u2uWAAAAAB1otSzVYtbhURsWPHr\n4wsnPHt5QzQXZ+GNl4zH2si6zI74XsrIiej0l6agTlL/to2lQ5aIzJYYs7WzhMliwpf5uhZ3\ndp6YMmVKPRWxdu3aenpz/cHOEwAAALXzeEcKk9m0d98f21ceYXVce3lcZ0V/xq4hMCCKTVyi\nkHCNKNsxjF4mSTaah5CvJ1W4EewYpr7+CYrZ1sxXEOwAAADqJGa3sZJUy+GlwruNRd0ujZ3W\nSFdM822wwxg7AAAAqC8SNis04DaZJMWDZ0NjJR3vkQleyv7dfGW/55GxGUOwAwAAgPphuhge\n0kcu3RfsP5FlSj14wY3j5GHdhfc+SP3cWHWl6fX41TcEOwAAAKgfsk7kN5yILNb2xHgym5Vh\nqccMuUwlMBjMYuCPr9Bbjch210GwAwAAgHoT+TlFvC2P3W21Rnj2Ai6U7flfjhEKLJVZ1rT1\n7u1T3+y5Mb4vKSmp/uoAAACAZohVUehzRKRO9HwiRatekg53yTJ+Emjzy9plDo2VtO6H5duu\ncuMfBDZ4BQAAAJ/oPEFees5SliYwRfbUp8bADhJlRCNa/cSH0BULAAAADUHMOiCMhOJmcoKD\n7cxa/vgHeh5zZIkIwQ4AAAAajJhspwhjezzJCe5YVn7RmrYBg+2IGmBLsaysrNTU1NLSUiJS\nq9XR0dE33HBDfX8pAAAANE7qRGlBco6UTTdaBrn7bHiCJHqkLHO7wGC7zF9NoV3YiD4tfbCd\nqD9/ZWVlTk6OwWCIi4tzvrphw4YlS5akpqY6nI+JiXn00UfnzJmjUCjEfDsAAAA0Pbr9rVQT\niNEWVx60WDu6+/RND8nLz1vK0p0G2/F0crVx4DJW2apF90Z68oc3m82ffPLJLbfcEhwc3LVr\n16efftr5nhkzZjzwwAPOqY6I0tLSFi1a1LNnz+PHj3vw7QAAANCEaX5h2VyWKQtQLPHgaVZC\nPZ/hZAFCg+2q+OMfGKwW0RU2ZW4Hu+PHj/fq1eu///3vvn37rFah/duIVqxYsXr16trfk56e\nPmzYMMHkBwAAAM1W+BLyH0ZBj1RoP/LsBcpWbOw0ueCl8vPWC9+36MF27gW7lJSU2267rfY0\nVlRU9NJLL7nytqKiogkTJpjNmMcCAADQckgo6keK/KZ1YqDHr2jdX9puuPBwsos/mgpSWm60\ncCPYabXacePGlZSU1H7bt99+W1lZ6eI7U1NTP/74Y9drAAAAgCaPUYp/R5dHOVUHoRjDU+pq\no75UuFOx2XMj2C1dujQjI6PO23799VfnkyqV6tZbbx0xYoS/v7/DJQQ7AACAlknM6ieslHo9\nq5AqBQbbGSv5Ex8Z+RYZ7VwNdkaj8dNPP3U4KZFIevToMXLkSNsZjUbz119/OdwWFxeXlpb2\nxx9/7Ny5s7CwcMSIEfZXL1y48M8//7hfOQAAADR5YrKdX2sm9gnhwXalZywXNrfEwXauBrtf\nf/21qKjI/oxarT5w4MCJEycWLVpkO7lnzx6j8bp/jgzDrFu3LjIysvpQqVRu27YtKirK/p4/\n/vjDk9oBAACg6fs323nSwqZOlEYNrWGw3RZT0ckWN0XW1WC3b98++0OJRPLnn3/27dvX4bbk\n5GSHM3fccUf37t3tzygUikmTJtmfSUlJcbEMAAAAaH7UCemtVH3lUk968LpO4QKjBfIMb6VT\nKw2GUl50dU2Jq8HuyJEj9of33XdfTEyM821Hjx51OHP33Xc733bXXXfZH545c8bFMgAAAKC5\nMabTpT5SyYkg/4dZpqju+6/HyihulkKqEBhsZyjnT6w0tKjBdq4Gu0uXLtkfPvzww4K3Obe9\n3XLLLc63tWnTxv6wesMxAAAAaInknUl1PxGZzXFEEg9e4BfJdJkkPNiuJNWSsU1gC7LmytUR\ni+Xl5faHHTp0cL4nNzc3Pz/f/kxwcHBsbKzzna1bt67l5R5bsGDB6dOniejOO++cMWNGLXfy\nPH/8+PE9e/ZcvHjx/9m788CYrv5/4OfOksm+SkwkEcSSpWIJVUGlEUtR9aBqeZ4G5aHUVkXa\nql3RaotW9as0eLrRUrHUWsS+JSELIppNIoTsk2Qy2/39Mc9vnunMZDIz986Syfv1173n3nPu\nZ45kfHLvOeeWlZXJ5fJWrVoFBATExMT06dOHx2vpb5oDAACwqNbbiFM/gefbimsmjooLiOGV\n31U8vqgjh3v4m8SjE8fnBVNSxmbH0AymtrZWfTcoKEj7nFu3bmmUREVFUZSOW6PV1dXqu3I5\nC2MbU1NTlVldk2pqaj7//HONm4tFRUVFRUXXr18PDg7+8MMPVbM9AAAAwOw4zsRzOiFE+BLv\nyTUTlxcOn8avypXVFmsOqqMVJGO7pN8GR76bjpzEzhj6KFbj4alUqiMj1phgQQiJiorS2Vph\nYaH6rqenp4FhNObKlSvr16835EyZTLZixQo90zUKCgqWLFlSWVnJMCQAAACwJK4j1X2BI1eg\na7BduSL9mwbSAuZRGJrYdezYUX1XIzNTSk5O1ihpLLF7+PCh+q6Hh4eBYajIZLLnz58/fPjw\n2LFjixcv3rBhQ0NDgyEVf/75Z42re3l5+fv7q99ZrKqqwrLJAAAAVsFkZTvXQE7ov3QPtnt+\nW553zP4H2xnad5GRkX/++adq9+zZsxpJ26NHj7Rvg/Xq1Utna7t27VLf7dy5s4FhqKxaterO\nnTvG1hKLxeovxmjVqtWKFSuCg4MJIVVVVWvWrHnw4IHy0I0bN4qLiwMCAoy9BAAAADDE5IFs\n4CBe+X15ySUd1XN+lnh14np2MeK1W82OoZ8tLi5OfXfr1q11dXXqJVu2bNEYKtehQ4cOHTpo\nN5WWlnbmzBn1ksZu7LHu5s2b6oMFJ02apMzqCCEeHh4zZsxQP/n8+fOWiQoAAAA0CF/icTn5\nAt5pE+pGTHdw8dfxQJZWkDtfNUhr7PmJrKGJ3cCBA9Vf81pUVBQbG/vXX38RQhQKxffff79l\nyxaNKq+++qp2O/fv3x87dqxG4YABA4wImYGsrCz13R49eqjvdu7c2dXVVbVr4FQMAAAAYJ/o\niI/bi54uE7icYmOrcgVUtwWOHL6OQ+IyRcZ2ex5sZ+ijWBcXl2nTpn311VeqkuvXr3fs2NHf\n37+6ulpjzqyS+jtk5XL5ihUrrl27dunSJY3BcAEBAbGxscbGvXjxYvUJHCKRaO7cuU3WKigo\nUG0LhUIfHx/1oxRFRUREqF6ekZeXZ2xUAAAAwA5xCoeqIIQ4C7bU1H9qbG23tpzOkwX3d+sY\nf/8sTV5wUhY8zD6XNjPiUy1cuHDnzp319fXqhSUlJTpPdnNze+WVV1S7Mpls3bp1Os/897//\nzeEY/bTb3d1dfdfBQfdISQ01NTWqbS8vL+0T1Kdx1NbW0jStc7kWAAAAMC+fj4joKHEdWfvX\nu6Y1EDyUV3FX/vSGjsF2D35o8AjheHayw8F2Rnyk9u3bb9iwwcCT58+fr/7oVk+bixcvNjwG\nhtQTOycnJ+0TnJ2dVds0Teu8EwkAAABmR/FJu1uk1UoFbfqaaC/MdHDy03GDRiEnGdvE0jo7\nfCJr3H3IuXPn3r9/f/v27fpPCwsLS0hIaLI1JyenPXv26EywzMSoxI4QIhKJ1Efd5ebm7tix\nQ7n9+PFjR0dH84QJAAAA/8VkhizPmeo23/H6inpaq4G6p3TWDkn3BQKm8dkY4xI7iqK++eab\n4ODg1atXa8yKVQkNDT127FiTt+vc3d0PHDigPW1CJBKpr0iizsHBYfTo0UYFrE4qlcpk//uH\n5fN1DKrUyNVEIpH6bkVFhfp8Xi63RbycBAAAoPny6MDp/KZD9o8S7UNPr8seneEGxdnVYDtT\nPszSpUsnT568Y8eOpKSkrKws5SonHA6ne/fukyZNmjVrlv6sjsfjjRs37vPPP9d4m4WSSCT6\n4YcfdFZ0d3dnktjx+Xwej6fK7XQuaKxRqDF074UXXkhKSlJunzlzZsGCBSYHAwAAAAZictOO\nENJuBL8iW1F6S0cL9/c0eHbkuLWzn8F2JmapgYGBq1evXr16tUKhKCsrUygUXl5eemYwcDic\nKVOm+Pv7h4aGjhw50tvb29SAGXFzc6uoqFBua8wC0Vmo8bhWIBColiz29PSkaTt8Ng8AAGBv\nKNJ1lsOVBEX9c4XGEYWM3N4s7rveiedkJ3Mlmd5+5HA4vr6+TZ7G5/MTExMZXos5V1dXVWKn\n81GyRmJnyPwPAAAAsHE8F6rbfMGNlfUKueahuqd01neSbvPsZLCd/dx7NISbm5tqW5XhqSsr\nK1Nte3l5acylAAAAAKsQ9nri7rTQWfBV06c2wqMjJ+QN3Y8Wn1yVFZ03/VGvTbG5AYNCofDw\n4cNmajw4OFj1Polnz56VlJT4+/urjioUiszMTNVu+/btzRQGAAAAGIFuIHk9nAXPFQq/+oa3\naWLibZf2r/HL78rL0rXu2hFy7/sG9/Yc9+Bmf8Or2X8Ao7zwwgvqu6qXTChlZGSoP5+NjIy0\nUFgAAACgByUgnjMIITQl4HFzTG+GQyLfFTh66Uh+FFKS8bVY3tDsR8+3rMSud+/e6sPmfvnl\nl0uXLlVVVdXW1qakpKi/MI2iqJdfftkaMQIAAIAW74VEuON51X2pvBuTZhzcqMi5DpSu9EdU\nRN/frWNVlObF5h7FmpWjo+PIkSP37dun3K2rq/v0U92vnxs0aFCrVq0sGBoAAAA0jutLPGe0\nfokwWfdEySuMGzLG4eFvOnK4ovMyr3BOmwE6VrptLlrWHTtCyJtvvtmxY0f95/j5+U2dOtUy\n8QAAAICFdfgH3+cF3W8ZuPu9tLa4GT+QbXGJHY/HW7VqVVRUVGMndOjQYcOGDerzZwEAAMBG\nCF9i4WEjxSFd3xUIvHSsXScX07c3ixWS5prbtaxHsUpubm7Lly+/c+fOn3/+mZubW1ZWJpVK\n3d3dO3XqFB0dPXDgQIqyk1UKAQAAQCeBBxU5R3DrEzGtuWgxERUp7v9HEv52s1zZjsLrE0yT\nlJQUHx9fWVlp7UAAAABaHOYj7ZRy9klyD0l1Hur6rqBNP1Puf7FyT9FkLe5RLAAAANgBLjeX\nEK27bUbq+IZDo4PtvpPUljS/m19I7AAAAKBZkeYJ28b7uoY7Ohxk2BLFIV3fceC76Rps10Df\n2SxW6L6dZ7uQ2AEAAECzQstI9T5CyV0d1zC/aSfw5kTOFhBdo+trChUPfmpg2L6FIbEDAACA\nZsWhE3F/gwheEIk/YKW9Vt257UboXruu4ISs9GZzeo0sEjsAAABoboTfkvbpYskEtjKZzhMc\nvDrrbirjW0l9abMZbMfaxI20tLTTp0+np6eXlZWJxWKj6p47d46tMAAAAMD+cTwIIcKXeGxN\nj6W4JHKe4MoHYmmNZg4nq6PvbBH3WeVENYc14liI8cGDBzNmzLhw4QLzpgAAAACswtGH0/Ud\nQepnYqJ1e64qV/Fgn6TLZAdrxGUcpjcwU1JSoqKikNUBAACA5bG7aJxvD27wUN0N5h+Tlt5q\nBoPtGCV2YrF4zJgxIpGIrWgAAAAArKjzPwWenXRlRzTJ+FZS/5zpJFxzY5TY7d69u7CwkK1Q\nAAAAAIzF7k07Dpd0nePId9ax/Imslk7f2qCQs3g19jFK7JKSktiKAwAAAMA0fO4NV8e1bLXm\n3JoK/7fu4XSVOYq/fpOwdSFzYJTk3rlzR6OkV69eCKCXvwAAIABJREFUixYtCg0Nbd26NYeD\ntVQAAADAzJ4t9XH7lBDSIBsmlfVipUlhH155nPzRGR2D6vIOS73DuD6Rul9EZnWMEruysjL1\n3aFDhx4/fpyidC3eDAAAAGAOzoNI2aeEEEf+AbYSO0JI6FuCyoeKmnzNQXW0gqRva+i7wdHR\nyxZvYDGKydXVVX133bp1yOoAAADAolyGEK85JOh0Tf16Flvl8En3BY48Jx2JjaSaTv9KQtvk\nPApGiV2bNm1U2xRFRUREMI4HAAAAwEitvyYucay36tyaipihe7BdxT157u9S1q/IHKPEbtCg\nQaptmqax7gkAAABYC7vTY//bZl9eYIzuZv86KCnLtLkpsowSu2nTpqnPkNCeSwEAAADQrIVN\nE7i11ZEv0QqS8XVDQ6VtvUaWUWIXGRmZkJCg2l21ahVN29bHAwAAgJbDHDftOHwS+a6AK9Ax\n2K6his7Y1mBTg+2YTuhYvXr1tGnTlNsXL16cOXNmTU0N46gAAAAAbIVrECc0Xvdgu7JMed4R\nGxpsxyixPXXqVEZGRlhYWHh4+N27dwkh33333dGjR2NiYsLCwpydnQ1sZ9GiRUzCAAAAAFAS\nvsR7ck1GUWKadmSx2cBXeBX35I8v6ljZ7uF+iXcox7OLTaxsRzF5eDpjxoydO3cyD6I5PsBN\nSkqKj4+vrKy0diAAAACgpvaMNH+ZVNazun4ruw3LxfTVj+prH+tIWgTenH4bHPluFDHP42DD\n2eLaegAAAACmoKXkyb/53OtODt9zOcXsts11pLotcOQ46BpsV67I2N5AbOA+FRI7AAAAsBcU\nn/gsIYSSyIYQqoH15t2COKH/1D3Y7lmaPP+Y9QfbIbEDAAAAO+IxlbRPE3Q9LJd3MEfzQYN5\nbfrpftia84uk4oGVp8gisQMAAAA7QgmIoJtZrxA+w8HZX8cDWYWcpG+18sp2jMb3TZ06tX//\n/myFAgAAAMAW5fRYc7TMFVDd3nW8vqJe8ffmy2RFxUX3iia5TP1PH28fb3NcukmMErvo6Ojo\n6Gi2QgEAAABoFtw7cDpPcri/V6LcLZM92vl89j3xRS+uv+SkeGnrmmnTpm7evNnwpd/Ygkex\nAAAAYJ/MuvJI8DC+X28eIaReUb3hyUgXjteXQfe+DLq3rW3e8tZnT+y5MnHiRPNdvTFI7AAA\nAACMR5GuMx2cWnFOV/+fM8dztl+iF9dfeSTYIXJR6wOnjv557tw5CweFxA4AAADsFyV35O+j\nKJE52ua5UN0WCLIazkW7vskhf3vzhCdX2NUp7tSpU+a4rh5I7AAAAMBOiVOFvl09Xf7p7LDD\nTFfwCOHI3Krcua20D7lz/MrLy8103cYY+uz5tddeU98NDAzcvn07IWTq1KnMg0hMTGTeCAAA\nAMDf8IOJ7AkhxFmwtbZhHsM5o41pF9L26b1c7fInsoevtB1kjivqYegnPHr0qPpuaGiocmP3\n7t3Mg0BiBwAAAOzj+hCveURWUv5oiZmyOkLIyNixq24sG+Q2w43royp82HAjR355zJhtZrpo\nY6z5nloAAAAA8/JdSwiRF5hlQTulN4b/89CZfetSh/7D84MOgl5Suj6z/tzvles/+PiDsLAw\n811XJyR2AAAAYOfMt1gxIYTL4f7ns0Nb927cd2z1o6ICHo8XHh6+Y8u2f/7zn2a6oh5I7AAA\nAAAY4fMdFr398aK3P66prW7b30UgEFgrEsyKBQAAAPtn1sWKVdxc3K2Y1RHD79jl5+er7/L5\nfOXGtWvX2A0IAAAAAExjaGIXHByss7xPnz7sBQMAAABgLmYdaWcj8CgWAAAAWga6wdlhp5fr\ncEIU1g7FXJDYAQAAQMvwdIG78zsC3mlH/iFrh2IuSOwAAACgZfCeRwiHJs4cTrG1QzEXLHcC\nAAAALYNDGGmzh3IZUnfT29qhmAsSOwAAAGgx3JWLBtvtFAo8igUAAICWxTJr2lkFEjsAAAAA\nO4HEDgAAAFoce71ph8QOAAAAwE4gsQMAAICWSPgSj6KquJwCawfCJiR2AAAA0PLQMvJ8ua97\nR3fnedYOhU1I7AAAAKDloXik9hyHqhTw/uBzU6wdDWuQ2AEAAECL1OpDwvURiT+WKzpYOxTW\n2OeUEAAAAIAmuLxKQgpcOS6ia/azXjH7iV1xcfHVq1dv3bp19+7dioqKqqoqsVj84MED5dH6\n+vrExMQJEyZ4e9vt2zwAAACgeeC4WDsClrGZ2B04cGDHjh1nzpxRKBSNnSORSObMmbN48eIP\nP/xwyZIlfD6fxQAAAAAAjCV8iffEXm7asTPGLicn55VXXhk3btypU6f0ZHUqdXV1y5YtGzZs\nWHV1NSsBAAAAAAALiV1qamqfPn3Onz9vbMWzZ8+++eabhiSCAAAAAOZjNy+iYJrY5efnDx48\nuKKiwrTqJ06c+OabbxjGAAAAAACEeWK3YMGC8vJyJi2sXbu2oaGBYRgAAAAATAh7PXVzep9L\nPbJ2IIwwSuxSUlKSkpI0CqOiopYuXfrLL78IhULtKm5ubt98842Xl5eq5OnTp8ePH2cSBgAA\nAAAj9ZfJXyEugi0ujpusHQojjBK7AwcOqO/6+fkdOHDg1q1bGzZsePPNNx0dHXVcj8N55513\n9u/fr1544sQJJmEAAAAAMOLYm/BaE0IE/FOENOMZsowSuz///FO1zeVy9+/fP2bMGEMqxsXF\n9erVS7Wbn5/PJAwAAAAARigH4ruBCL99Xn2nWb++gVFiV1xcrNoeNGjQwIEDDa/bv39/1XZB\nQQGTMAAAAACYcp9IPGe2fsnZ2nEwwiixe/bsmWq7R48eRtX18fFRbRcWFjIJAwAAAAAIw8TO\n1dVVtf306VOj6qoncw4ODkzCAAAAAGBLs17TjlFi5+fnp9o+d+5cXV2dgRVlMtnly5d1tgMA\nAAAApmGU2EVFRam2CwoK/vWvfxm4It2yZcvu3r2r2o2IiGASBgAAAAAQhondyJEj1XcPHjzY\nqVOnbdu25eTk0DStfX5lZeWhQ4deeumljRs3qpe/+uqrTMIAAAAAYJHyaSxFaq0diNEYPUV+\n/fXXg4KCHj363xrNjx49evfddwkh7u7u9fX1qvIePXrk5eVVVVVpN+Lp6Tl27FgmYQAAAACw\nqeGuh8t6Ae/Ms+p7NO3a9Pk2g9EdOycnp08//VTnoerqaqlUqtq9ffu2zqyOEPLRRx95e3sz\nCQMAAACATdU/OPF/4FBPnB12WTsU4zB9V+yECRM+/PBDk6uPHTv2vffeYxgDAAAAAJu83yMc\nF5kiXKboYO1QjMM0sSOErFu3bv369SYsWTJlypQff/yRw2EhBgAAAADWcFuR4Gu88IwG6WvW\nDsU47CRVCQkJKSkpr7/+OpfLNeT87t27JyUlJSYmCgQCVgIAAAAAYJPgBbbSJEtibQm+F154\n4dChQ48fPz5w4MD169dv3rxZVFSkWtmOx+N5e3t369atT58+I0eO7NOnD1vXBQAAADAT4Uu8\nJ9dk1o7CCCyvrdymTZu5c+fOnTtXuSuVSqurqwUCgfo7KgAAAADAHMx7j5HP5/v4+CCrAwAA\ngGaqeb1hrPk9PAYAAAAAnRgloQcPHrx58yY7cfB4QqGwS5cuAwcO5PP5rLQJAAAAwJzwJV7Z\nrasU3SCR97d2LE1glNgdP358586dbIWi5OnpuXTp0kWLFiG9AwAAAOujZaRolI/rcZn8hec1\nqYRQ1g5IH5t7FFtZWfnBBx/ExcXV1ja/F7QBAACAvaF4hOtJCOFxMx1456wdTRNsLrFTunDh\nwsSJE60dBQAAAAAhPh8R55hy0QmJLNbaoTTBRhM7QsiRI0cOHjxo7SgAAACgxRNEkLbnvHsN\ntXYcTTN7Yufs7ExRJj6N/vzzz9kNBgAAAMCOMUrsvvvuO5qmy8rKhg79Ww47aNCgn3/+OS0t\nrby8vLa2ViwW5+TknDx5csqUKRqvlF2zZg1N0zRN19XVXb16deHCheqvjr1y5crDhw+ZRAgA\nAADAFttf046iaZpJ/cePH/fr1y8/P1+527dv3++++y4iIkLP+e++++7vv/+uKvnxxx8nTZqk\n2j1+/PiIESNUUe3Zs+ett95iEqGZJCUlxcfHV1ZWWjsQAAAAsJwm3zBm3eSP0R07iUQyevRo\nVVbXq1evc+fO6cnqCCFt2rT57bffYmP/N/Zw7ty5z549U+2++uqrU6ZMUe2mpqYyiRAAAACA\nRTZ+045RYvfTTz+pL1D81VdfCQSCpi/J4Xz22Weq3fLy8m+//Vb9hPj4eNV2SUkJkwgBAAAA\nWg5GiV1iYqJq28XF5aWXXjKwYs+ePd3c3FS7hw8fVj+qfs+vqqqKSYQAAAAA7BJ2Pe/jOoDP\nY+flW+xilNhlZ2ebXJfH+9+dTNXDXCX1d05IJBKTLwEAAADAsvpL5NFgPu+aq+ATa4eiA6PE\nrrq6WrVdW1t76dIlAyveuXOnoqJCZzuEkPv376u2PTw8mEQIAAAAwCan/sTpJeWLxShic7ef\nGCV2AQEB6ruzZs0yZJaoWCyePXu2eolQKFTf3b59u2q7VatWTCIEAAAAYFnrraRdakXt7zRx\naPpky2KU2EVFRanvZmVl9ezZ88cffxSLxTrPp2n6xIkT/fr1u3Llinp5586dlRu1tbXLly/f\ns2eP6lDXrl2ZRAgAAADAMsfexLG7bU6PZRRTfHz8vn371Evy8vL++c9/zp07d8yYMR06dAgI\nCBAKhdXV1UVFRYWFhYcPH87NzdVuZ+zYscqN2bNn7927V/2Q4RMyAAAAAFo4RondsGHDXnnl\nlXPnzmmUV1RU7Nq1y8BGWrduPWHCBOW2QqFQPxQcHNy7d28mEQIAAACYifAlXpPrFVsYo0ex\nFEUlJia2bt2aSSNfffWVp6enzkPz5s0z+T2zAAAAAC0No8SOEBIcHHzp0qX27dubUJeiqK+/\n/vqNN97QeTQ8PHzOnDnMogMAAAAwI1sbacc0sSOEdOzY8fbt2/PmzeNyuYbXCgkJOXHiRGOp\nW1BQ0OHDhw15jwUAAACAVSn43BRrx/BfLCR2hBB3d/ctW7Y8ePBg2bJl7dq103c9DmfgwIG7\nd+/OzMwcMmSIzqZmz56dlpYWEhLCSmwAAAAA5lJzSOgX6e06kMsptnYohBBC0TTNeqOPHz++\nceNGbm5uZWVlZWUln8/38PDw8fGJjIzs0aOHq6trYxVFIpGeozYlKSkpPj7ekHX7AAAAwG5V\n7iBPZhJC6hrmVtd/Qaz9cNYs127Tps3o0aNNqNhcsjoAAAAAQgjxmErKPyNOfeuKZlo7FELM\nlNgBAAAAtAgUn7TPIJSjrMAm1j1hZ4wdAAAAQAtFORJrP4FVYS2ItLS006dPp6enl5WVNfZK\nscZoL3EMAAAAAMZiIbF78ODBjBkzLly4wLwpAAAAgGbKFl5EwTSxS0lJiYmJEYlErEQDAAAA\nACZjNMZOLBaPGTMGWR0AAACALWCU2O3evbuwsJCtUAAAAACaNatPoWCU2CUlJbEVBwAAAAAw\nxCivvHPnjkZJr169Fi1aFBoa2rp1aw4Ha6kAAAAAWA6jxK6srEx9d+jQocePH6coillIAAAA\nAGAKRjfVNN4Atm7dOmR1AAAAANbCKLFr06aNapuiqIiICMbxAAAAAICJGCV2gwYNUm3TNI11\nTwAAAACsiFFiN23aNPUZEtpzKQAAAADAYhgldpGRkQkJCardVatW0TTNOCQAAAAAMAXTFUlW\nr149bdo05fbFixdnzpxZU1PDOCoAAAAAMBqj5U5OnTqVkZERFhYWHh5+9+5dQsh333139OjR\nmJiYsLAwZ2dnA9tZtGgRkzAAAAAAgBBCMXl4OmPGjJ07dzIPgq0HuAkJCcr88tVXX33nnXca\nO+3o0aM7duxosrWVK1f27NmzsaNJSUnx8fGVlZWmhQoAAADAOvt5OURqaqoyq2vSo0ePzB0M\nAAAAgOVZ+VW1bLly5cqXX35p4MmFhYVmDQYAAADAKpprYieTySorKysrK7Ozs8+fP5+dnW14\nXdyxAwAAALvUXBO7VatWmbZsXlVVVXV1tXJ79OjRcXFxjZ3p5+dnYnAAAAAA1sAosZs6dWr/\n/v3ZCsUy1J/Ddu3atW3btlYMBgAAAIBFjBK76Ojo6OhotkKxDPXnsMjqAAAAwJ4010exixcv\nlkqlql2RSDR37lxDKqoSO0dHRz8/v+zs7PT09OLiYoqi/Pz8evToERoaapaIAQAAAMysuSZ2\n7u7u6rsODg4GVlQ9inVzc9uwYcPVq1fVj/78889dunR59913g4ODWYkTAAAAwGKsn9h98skn\ndXV1a9eutczlVInds2fPnj17pn1Cdnb24sWLV65cGR4ernGourr6/v37yu28vDwul2vWUAEA\nAACMwlpiV1FRcfbs2aKiIsNfxlBRUXH16tUbN25MmTKFrTD0q6mpqaqqavI0sVi8cePGbdu2\nubq6qpfn5OTMnj1btevo6Mh+iAAAAACmYiGxKy8vT0hI+P777+VyOfPWRCLRH3/8ofOQg4PD\n6NGjmTSusTRxZGTk6NGju3TpUl9fn56evmfPHlXaV1FRceTIkYkTJ6qfLxQK4+Pjlds5OTm/\n/PILk2AAAAAA2MU0sSsvL3/llVfS09NZiYYQIhKJfvjhB52H3N3dGSZ2MpnspZdeUm47Ojq+\n++67ysF5bm5ucXFxISEhCxcuVCgUyhOSk5M1EruAgADVFI2kpKS9e/cyCQYAAACAXUwTu4UL\nF7KY1Zlbt27dunXr1tjR9u3b9+7d+/r168rdx48fV1dXa8zSAAAAALBZHCaVHz58aGd3rTRW\ntquoqLBWJAAAAADGYpTY7d+/n3kE7u7uffv2Zd4OKzQmunI4jPoHAAAAwJIYPYrNzMxU323f\nvv3KlSv9/PwuX778ySefqAarrVmz5uWXXy4sLMzPz9+/f39GRoay3MHB4ciRIzExMeqr0AmF\nwsOHDzOJqjH19fWffPKJardfv37Dhg3TOKegoEB918vLyxyRAAAAAJgDo8SuqKhItU1R1LFj\nx8LCwgghw4YNE4vFmzZtUh7KyclZtmyZcvujjz46cODA5MmTJRKJRCKZOXPmjRs3fH19mYRh\nIEdHx7t376reV1FXVzd06FCKolQnFBQU3LhxQ7Xbrl07jeVOAAAAAGwZo0eNxcXFqu3Q0FBl\nVqc0ePBg1fbhw4dlMplym6KocePGbd26Vbmbn5//3nvvMYnBcBRFde/eXbWbk5OzadOmwsJC\nmUxWXl5+9uzZ5cuXq6/YEhsba5nAAAAAAFjB6I6demInFArVD0VFRam2Kysrs7Ky1KejTp8+\n/b333qurqyOE/PDDDwsWLFA/33zGjh178+ZN1e7FixcvXryo80yhUDh8+HALhAQAAADAFkZ3\n7NSnGlRXV6sf8vHxadeunWr32rVrGhXV87zGFq5jXXh4+IQJE5o8zc3N7eOPPzb8/bPARFZW\nFvV36rd7AQAAwHCMEjv1Nd5yc3MbGhrUj6rfhDtz5oxGXdXUCp1HzWfSpEmzZs0SCASNnRAR\nEfHll18GBQVZLCQAAAAAVjBK7Dp16qTarqio+OKLL9SPqid2J06cePLkiWr3+fPnqrmxROtN\nX+Y2fPjwnTt3vvXWW127dvXw8OByuS4uLkFBQcOGDVu7du369ev9/PwsGQ8AAAAAKxiNsevd\nu7f6GLUPP/zwwoULw4cPj4+Pd3d3HzBggOqQSCR64403vv76606dOuXm5s6fP185wE6Jz+cz\nCYMQ4ubmZtQiKR4eHuPGjRs3bhzD6wIAAADYDkZ37MaPH69RcuLEiXnz5uXl5RFCoqOj1W99\nXbp0qXv37i4uLl27dj179qx6rS5dujAJAwAAAAAIw8SuT58+/fr1a7RpDmfUqFGGtBMeHs4k\nDAAAAAAgDBM7QsiuXbvUp1BoeOeddwx5K9fUqVMZhgEAAAAATBO7Ll26nD171t/fX+fRnj17\nfvDBB/pbePvtt6OjoxmGAQAAAAAsvOQ+KirqwYMHq1evDg0N1T66du3atWvX8ng6ZmlQFDVz\n5sxvv/2WeQwAAAAAQNE0zWJzxcXFOTk5PXr08PDwUC8vKirav39/dnZ2bm5uaWmpj49Pr169\nJk2apP6Or+YlKSkpPj6+srLS2oE0e1lZWS+88IJ6SVxc3OnTp60VDwAAQPPFaLkTbQEBAQEB\nAdrlgYGBFnsnLAAAAEDLxMKjWAAAAACwBUjsAAAAAOwEa49iGxoabty4kZGR8fz58/r6eqPq\nrl+/nq0wAAAAAFosFhI7kUi0du3a7du3V1dXm9ZCM03s6urqIiIiqqqqampqamtrnZ2d3dzc\n3N3dQ0JCunbt2r1792HDhmlMImFRfX39/v37//zzz+Li4qKioqKiIi6X6+3t3aZNm759+w4Y\nMGD48OEODg5sXS4/P//w4cMXL14sKSl58uRJSUkJRVHe3t5+fn49e/bs3bv3yJEjG1v1BgAA\nACyEZqaoqCgsLMy6MVjFoUOHmvxcDg4Oo0aNSk5ONqplsVis0c6mTZvUT8jLy5s3b56np6f+\nqwuFwrVr14rFYiYfUyqVbtu2rVu3bk1+WC6XO2TIkGPHjhl7iczMTI2m4uLimMQMAADQYjEa\nY6dQKMaMGXPv3j0mjdgxiURy+PDhgQMHjhs37tmzZ6y0uXfv3sjIyK1btza50sqTJ0+WLVsW\nFRWVnp5u2rVOnjzZrVu3OXPm3Llzp8mT5XL5qVOnRowYERMTk5WVZdoVAQAAgAlGid2vv/56\n48YNtkKxYwcOHBgwYMCjR48YtrNu3br4+PiamhrDq2RlZcXGxt69e9eoC9E0vXDhwmHDhhlb\nkRCSnJz84osv7t6929iKAAAAwBCjxO6XX35hKw67l52dPXLkSKlUanILO3bsWLZsmQkVy8rK\nhg8fXldXZ+D5Mpnsrbfe2rx5swnXUqqrq5s6dWozHToJAADQfDGaPKF9u87DwyM+Pr5Lly6B\ngYFcLpdJ47aPoqhevXoFBgYGBAR4eno+ffr08ePHxcXFGRkZcrlc+/z09PRNmzY1+fJcndLS\n0ubPn69eEhER8cYbb/To0SMgIMDBwaG0tPTmzZsHDx68efOmdvWCgoINGzasXr3akGu99dZb\nP//8s85DfD4/Ojq6Y8eOrVu3FovFjx8/vn79el5ens6TP/zwQxcXl3nz5hlyUQAAAGABkwF6\nGpMuu3Xr9vz5c5YG/9m6Q4cOeXh46Dz06NGjFStW6JwiGhAQoFAo9LesPXli2bJlISEhqt2Q\nkJDjx483Vv2PP/4QCoXal/by8pJKpU1+rsYeobZr1+7777+vrq7WrpKenh4fH8/h6Lj7y+Px\nbty4of+KmDwBAADAFkaJnY+Pj/r/xxcuXGArLNunJ7FTqqioePnll7VzncuXL+tvWTuxc3Z2\nVm0PHjy4trZWfwv5+fmtW7fWvvTp06f1V8zLy3N3d9euOG/evIaGBv11U1JS2rVrp123c+fO\n+usisQMAAGALozF2QUFBqm3lc0kmrdkZT0/P3377zdvbW6NcO49pkmp43MCBAw8fPqye5+kU\nHBy8c+dO7fK0tDT9FRcvXqy9GOHmzZu3bNnS5JJ4PXv2vHHjRo8ePTTKHzx4gIkUAAAAlsEo\nsRsxYoRqm6Zpo2ZrtgS+vr4TJ07UKCwpKTGtNTc3t7179zo6Ohpy8siRI7UXn9N/6YKCgt9/\n/12j8J133tEY26eHr69vUlKSn5+fRvm6det0DjoEAAAAdjFK7KZPn66eZ2DpE23a2VWT6881\nZvny5W3btjX8/JEjR2qUVFRU6Dn/66+/1ki/goKCvvzyS8OvqKyydetWjcLCwsJLly4Z1Q4A\nAACYgFFi165duy1btqh2ly5dqj0+rIXTOWTNBM7OztOnTzeqSmhoqOEny2Qy7ae3q1evFggE\nRl2UEDJ+/HjtB7La9wIBAACAdYwSO0LIv//9788++0y5ssndu3eHDh364MEDNgKzEw0NDay0\nM3bs2CbfIaZBe3ifHrdv39a4lejm5jZhwgSjrqhEUVR8fLxG4ZUrV0xoCgAAAIxixDp2U6dO\nbexQx44ds7OzCSEXLlyIiIgIDQ0NDw9vcoy/SmJiouFhNC9svVzrlVdeMbaKUesIaj8qHTly\npIHj+bSNHDlywYIF6iXKtf3sfmlDAAAA6zIisTNwbqNMJsvMzDRq7qe9JnaFhYX/+c9/WGmq\nb9++rLTTmIsXL7J4xQ4dOvD5fPXXbIjF4oKCgg4dOpjcJgAAADSJ6aNY0EkkEm3fvn3gwIEm\nz4HVoL5AsTlor4Ri1BA9DRRFac+NraqqMrlBAAAAMASjV4qBkkQiycvLe/jwYXZ2dmZmZkZG\nRmZmJovzSFxdXfl8Plut6VRWVqZR8s477zg5OZnc4JMnTzRKsBoOAACAuSGxM119ff2QIUNy\ncnIKCwsVCoX5LmTstAljyWQy7XWJ//rrL3avon0JAAAAYBcSO9NJJJLTp09b4EI8nnn/mfSv\nb8cW3LEDAAAwNyMyhmvXrpkvDrtEUVRAQEBRUZG1A2mCZe6l4Y4dAACAuRmR2PXp08d8cdgT\nPz+/rl27vv7662PGjLl06ZJpq8FZkqurqwWugjt2AAAA5oZHsUy1bdu2e/fuoWq8vLysHZRx\ndAZcXl7e7D4IAABAC4fEznTOzs65ubmtW7e2diBMOTg4ODs719XVqReWlpYisQMAAGhemK5j\nJ5PJ7t+/f+jQIf3nfP7550lJSQ8fPmR4OZvC5/PtIKtT0n7/2NOnT60SCQAAAJjM9MTu3Llz\n8fHxnp6eYWFhEydO1HOmXC5///33R48e3alTp27dun322WcaN4fA6iIiIjRK7t69a5VIAAAA\nwGSmJHaPHj0aNWpUbGzs3r17a2trjaqbnp6+ZMmSiIiI48ePm3DpZkckElk7BINER0drlJw/\nf94agQAAAIDpjB5jl5qaOnjw4PLyciZXzc8L8GJkAAAgAElEQVTPHzly5K5du6ZMmcKkHduX\nl5dn7RAM0q9fP42Sc+fOyWQy05bQ++uvvy5duqReEhIS0r9/f9PjAwAAAAMY9992ZmbmoEGD\nKisrmV9YoVBMmzbNy8vr9ddfZ96azWou97369OnD5/OlUqmqpLS0dP/+/ZMmTTKhtXnz5v3x\nxx/qJZs3b0ZiBwAAYG5GPIqVSqX/+te/WMnqlGianjVrFsObf7bszp07ly9ftnYUBnF1dR0z\nZoxG4caNG014VVpKSopGVkcIGTZsmOnBAQAAgGGMSOy2bNly+/Zt7XIulxsXF6enIpfLHT16\ntM61M548ebJ+/XrDY2hGJBLJ22+/be0ojDBv3jyNkvT09NWrVxvbzqpVqzRKIiIiunTpYnpk\nAAAAYBhDEzu5XP7VV19pFAoEgvXr1xcXFx85ckRPXR6P9/vvvz9//vzixYu9evXSOJqYmCgW\niw2PuFmQSqVTpkxJSUnRPiSTySwfjyGio6N79uypUbhmzZoDBw4Y3sjWrVu1fxgSEhKYBgcA\nAAAGMDSxO336dGFhoXqJv79/cnJyQkKCgWu5cTic/v37X7x4ceDAgerlZWVl2k/umrWCgoKh\nQ4f+/PPPOo8+ePDAwvEY7tNPP6UoSr1EoVCMHz/+s88+o2laf12apjdu3Lhw4UKN8pCQENt/\nqRoAAIB9MDSx054E8O2335rw9lhHR8ctW7ZoZA9Xr141th3bVFhY+NFHH4WFhZ07d66xc06f\nPn306FFLRmW4QYMGzZ8/X6NQoVAsWbKkW7duBw4c0HlvVSwW79y5MyIiIiEhQWNMHpfL3b17\nt2lTawEAAMBYhv6Pe+XKFfXd3r17jxo1yrRLduvWbdSoUUlJSaqS69evm9aUdclkslu3bolE\notLS0rS0tMuXL1++fFkjswkPD79375767S6apl977bXY2Njw8HCxWPzVV185OjpaPPZGrV+/\n/syZM5mZmRrlGRkZ48aNc3JyGjBgQEhIiK+vr1QqLSwsfPToUWZmZmMzYD7++GNMhgUAALAY\nQxM7jeewL7/8MpOr9u7dWz2xKy4uZtKatdTW1vbu3VvPCSNGjPjll1/Gjh176tQpjUNnz549\ne/YsIWTz5s1mDNF4jo6Op0+ffvXVV3VOlKmvr9f+LI2ZP3/+8uXLWY0OAAAA9DH0UazGLZmQ\nkBAmV+3QoYOexu3DnDlzkpKSXF1dlyxZwuEwfSevJQmFwuTkZI2hkEbhcDgrV67cvHmzxjN3\nAAAAMCtDE46Ghgb1XQcHByZX1Vj6xNj3ktm43r17X7hw4euvv+ZyuYSQQYMGbd261dpBGcfd\n3f3UqVObNm3y9PQ0tm5ERMSVK1dWrFhhjsAAAABAD0MTO19fX/Vdhg9PS0pK9DTeTHE4nH79\n+v3000/Xr18fMGCA+qE5c+YcO3YsLCxMo4qvr6/N3sxzcHBYtGjRw4cPFy5caODE5+jo6B9/\n/DEtLc2EWTUAAADAHNXkMhZKUVFRqampqt2XX345OTnZ5KtOnz59165dqt0ePXqoN94sJCUl\njR49un379m3atAkMDIyNjX399df1J0ByuTwrKyszM1MkEvn6+kZGRjJ8ot0kkUhUXV1d9f/5\n+PhoryNoCJqmb968efTo0Vu3bpWWlj59+rS0tFQgEHh7e/v4+HTt2rVfv34DBw7s3Lkz6x8B\nAAAADGfo5ImQkBD13OvixYtZWVkREREmXLKqqurgwYPqJcHBwSa0Y3UeHh65ubmGn8/lciMj\nIyMjI80XkgZXV1dXV9c2bdowbIeiqBdffPHFF19kJSoAAAAwE0OfA2q865Om6bffflsikZhw\nyQULFlRUVOhpHAAAAABMYGhiN3z4cI0ZjtevXx8xYkR1dbXhF1MoFPPnz9+9e7dG+ciRIw1v\nBAAAAAB0MvRRrFAo/Mc//qHxCPXMmTOhoaHr16+fMGGCQCDQU52m6bNnzy5evDgtLU3j0PDh\nwwMCAowK2hZgIQ8AS1IoFBqTrgzk5eXl7OzMejwAALbJ0MkThJCcnJyIiAipVKp9yMPDY9So\nUS+++GLXrl39/Pzc3d05HE5NTU15efm9e/dSU1MPHz6sscSxEpfLTU9PDw8PZ/QhrOHZs2dJ\nSUnTp0+3diAALYJIJDpx4oQJCy2FhYV16tTJHCEBANggIxI7QsjHH3+8du1aFi+/ePHiTz/9\nlMUGAcAuiUSiU6dOmZDYdenSxcYTO7lcLpPJjK3F4XD4fL454gGAZs24t7OvXr26oKDgP//5\nDyvXHj9+/IYNG1hpCgCgmbp///7du3eNrSWRSCZPnmyOeACgWTMusaMo6vvvv+dwOHv27GF4\n4QkTJuzZs8dml+cFALAMBwcHJycnY2sZ9bCleXn48KEJ/zU4ODgEBgaaIx6A5sW4xI4QwuPx\ndu/ePXz48FmzZmmsWmIgDw+Pbdu24W9NAADQlpqaasJ8ly5dupgjGIBmx+jETmn8+PFxcXE7\nd+785ptvCgoKDKwVFBQ0a9asGTNm2Mc7xAAA1OXk5JgwWq60tNQcwegkFosfP35sQsWgoCAT\nhvTt27fPhHtvJvQhAKiYmNgRQry9vZcsWbJo0aLk5ORLly5duXIlJSWlrKxM/QEBRVHe3t5R\nUVF9+/bt169fbGwsl8tlI2wAAJuTlpZmwq2myspKT09Pc8Sj7dmzZ+np6cYmW3K53NXV1c/P\nz6haNE1zOBwTnjKbtvS9XC5vaGgwthaXy+XxTP9/EMAGMf2B5nK5sbGxsbGxyl2FQlFZWVlR\nUUHTtLe3t6enJ0bRAYAVXbt27fbt28bW6t27d7t27cwQjvVxOBxjv5YVCoWZgmHRzZs3MzIy\njK0lk8kmTpxojngArIXlv1Q4HI63t7e3tze7zQIAmMa0m0ZVVVXPnz83tpYlEyCJRPLrr78a\nW0ssFru5udnl39um/UPX1dWZIxgAK8ItaAAATRkZGXl5ecbWsvDgMBPyGLlcbo5IAMB2ILED\nANBEUZQJQ6/wpkEAsDokdgAA0ASJRGLs1AQ7XmkPwJYhsQMAAH3kcnlycrKx73OjKEoikZjw\nvBgAmEBiBwAATeDxeCakaCasPwIADNnh3CgAAACAlgl37AAAoIWSSCT79u0ztpZCocDqd2Cz\nkNgBAEDL5eLiYmwVrH4HtgyPYgEAAADsBO7YAQAAGEEqlZrw2g+pVDp+/Hi8mhbMDT9hAAAA\nRqBp2oQ5whRFYW0/sAA8igUAAACwE0jsAAAAAOwEHsUCAAAAIYTk5+crFApja7m6uvr5+Zkj\nHjABEjsAAACzk8vl2dnZXC7XqFoKhSIkJMTR0dFMUWm4ceOGs7OzsbW6dOmCxM52ILEDAAAw\nO4VCkZuby+EYNwJKKpUKhUKLJXZgB5DYAQAA2BWapvPy8kyoaMJzWLA1SOwAwKJompZIJMbW\nMqEKQIsll8tTUlIEAoEJFc0RD1gSEjsAsKiSkpLLly8b+0BKJpNRFOXg4GCmqADsj7G/ZWAf\nkNgBgKUJBAJj/8vhcDhSqdRM8QAA2A2k8wAAAAB2AnfsAAAAbJRcLj958iSfzzeqFk3TMpnM\nhPeegR1AYgcAAGCjaJrm8/nGpmgKhUIkEpkpJLBxeBQLAAAAYCeQ2AEAAADYCSR2AAAAAHYC\niR0AAACAncDkCQAwkVQqra+vN7ZWXV2dOYIBAACCxA4ATJaXl5eVlWVsrfr6eicnJ6yJDwBg\nDkjsAMBEFEUZu7wWwVtfAQDMCX80AwAAANgJJHYAAAAAdgKJHQAAAICdQGIHAAAAYCeQ2AEA\nAADYCSR2AAAAAHYCiR0AAACAncA6dgBASktLRSKRsbWePHlijmAAAMBkSOwAgFRVVWVnZxtb\nq7Ky0tPT0xzxAEAzUllZmZuba2wtZ2dnoVBojnhaOCR2AAAAYLoHDx54eHgYW6uurm78+PHm\niKeFwxg7AAAAADuBO3YAdqWysrK2ttbYWjU1NeYIBgAALAyJHYBdKSkpMWG0XE1NjZeXlzni\nAQAAS0JiB2BXeDwej2f07zVFUeYIBgAALAxj7AAAAADsBBI7AAAAADuBxA4AAADATiCxAwAA\nALATmDwBYKPy8/Pr6+uNrYWFSwAAWjIkdgBmJxKJFAqFsbVu3Ljh7OxsbC285gsAoCVDYgdg\ndocPHxYIBMbWamhoMCGxAwBoFiQSya+//mpsLblcPmHCBHPEYzeQ2AGYHY/HMyGxE4vF5ggG\nAMBGODk5GVulrq7OHJHYEyR2AIZSKBQlJSWmVWQ9GAAAAG1I7AAM1dDQcPXqVQcHB2MryuVy\nc8QDAACgAYkdtETl5eVnzpwx9j1aNE3TNM3hYJEgAACwUUjswFZIpVITVvcoKSlJT083tpZE\nInFycjL23ptMJjMhQgAAAItBYgf6lJSUmJbKmDBX4PHjx4WFhcbWEolEPj4+xtZSKBQ0TRtb\nCwAArEsqlZowl1Ymk02YMMHYpzTNFBK75kcikZiQlJSVlZlQ68KFCy4uLsbWqq6udnd3N6GW\nCQuw4RYaAEDLQdO0CXNp6+vraZpuIYkdhfsWphGLxX/88YdyULxcLjfhDpBCoWhoaLDYMC/T\naikUCi6Xa2wtuVxu47UUCgVFUSb8ktv+R7P9WqZ1vsk/+bbfIRbufEKIsd2IzmerFofDMfYn\n3+R/Mnv99ja5lqOjo7G1CCFcLteE/ylMG4pt1IViYmJ8fX11t4PEzjR37twZOnSoRCIhhPj6\n+vr7+xvbk66urnK53NgbTlwu193dvaKiwqhahJBWrVo9f/5cue3s7CwQCKqrq5ucrent7V1e\nXm7stUyr5ePjU1ZWZmwt9c9lOHd394aGhoaGBqNqOTg4ODo6VldXG3i+q6srn8+vqqry8vIy\n4aOZ1iG23/menp61tbVSqdSoWno6383NjcfjVVZW6vwdNC1IS3ajJa9lWuc7OTlxuVwOh8Ph\ncCorKw2sRVGUt7e3xX6uLNmNJteqrKxs8lvX09OTpumqqirlrqurq0KhMHbxNi6X6+npacmP\nZrGffNNqtWrVqry8XH3lKS8vL7lcrv/73M3NzdnZ2djfFw6HIxAITHialJOT8/jxYwMvsW3b\ntjfffFPnUSR2LdGaNWuSkpL279/foUMHa8diz95///3z58+fPHnShFGAYLgZM2akpaVdu3aN\nx8PYEjMaP37806dPk5OTrR2InRsyZIijo+Phw4etHYid69OnT5cuXfbu3WvtQNiHhRsAAAAA\n7AQSOwAAAAA7gScXLZGnp2dAQACeW5mbt7d3QEAAFjQ2N19f34CAgBYy382K/Pz8TBi0Dsby\n9/c3YbkoMFZAQEBjkw+aO4yxAwAAALATuJcAAAAAYCeQ2AEAAADYCSR2AAAAAHYCw+ft37Nn\nz/bv35+Xl1dUVOTs7BwUFBQZGTlq1Cg+n69xZnFx8ZEjR27fvv38+XNHR0ehUPjyyy8PGzbM\nwcHBKpE3U8XFxXPmzFEoFDNmzHjttdd0noB+Zg7daD74GTYrfCdbRn5+/q+//lpYWPjkyRMP\nDw9/f/8BAwbExcVpT2izs37G5Ak7d/78+W+++UYsFmuUC4XChIQE9QWKU1NTN2zYoH1mp06d\nVq9ebcIbY1umurq6devWZWRkEEJ0/qeIfmYFutF88DNsVvhOtoxff/31hx9+0M5w2rdvv379\nemdnZ1WJ/fUzd+XKldaOAczl2bNnK1euVL04y9XVlRCifKGNSCTKyMgYMmSIcv2CysrKZcuW\nqd5a4+XlRQiRyWSEkPLy8rKysr59+1rlIzQLNE2LRKInT56cO3fu66+/zs3NVZZHRUV16dJF\n/Uz0MyvQjazDz7Bl4DvZMu7fv//FF18oszoul+vr6yuRSJTvE6usrHz69Gm/fv2UZ9plP2OM\nnT37+eefld8gPB5v2bJlP/300759+8aPH688Wlxc/Mcffyi3jx8/XlNTQwjhcDjLly/fs2fP\nvn37YmNjlUeTk5NLS0ut8Qmahxs3bkyePHnu3LmJiYlPnjzRcyb6mRXoRtbhZ9gy8J1sGb/+\n+qsyq2vbtu3333+/c+fOvXv3RkREKI9eunRJ9SJXu+xnJHb2TPVnd2xs7IsvvkgI4XK5kydP\n9vf3V5bn5OQoN65evarciIqK6tWrFyGEoqgpU6YoC2mavn79ugUDt1voZ1agG60Inc8EvpMt\no6CgQLkxZswY5U04FxeXsWPHKgtpmlb9Q9hlP2PyhN2iabqoqEi53alTJ1U5RVHt2rUrKSkh\nhChPkEgkql8D1d80hBBPT8+2bdsWFhYSQh48eGCxyJudTp06JSQkKLfFYvHmzZt1noZ+ZgW6\n0RzwM2wB+E62DKlUWlNT4+joSAgJCAhQlau/z0P5vNte+xmJnd2iaXrUqFHKbY1RMrW1tcoN\n5bDQ6upq1QjT1q1bq5/p5+en/OGurKw0d8DNl7e3d3R0tHJbNVZDG/qZFehGc8DPsAXgO9ky\n+Hz+vn37tMtv3Lih3PD29u7YsSOx335GYme3OBzOW2+9pV1eWlp679495Xbnzp0JISKRSHXU\nyclJ/WTVrup7B0yGfmYFutGK0PlM4DvZKh4+fJidnZ2RkaF86urs7JyQkKB8Vbq99jMSu5ZF\nJBJ9+umnUqmUEOLo6Dhy5Ejy9x9ujZdPq3641c8B06CfWYFutCJ0PuvwnWxuf/7557Fjx1S7\nM2bMCA0NVW7baz8jsWv2nj59qvprT6Vjx46BgYEahSUlJZ988olySAGXy126dGmrVq0IIXrW\nMqQoSrmhnP7dkhnez41BP7MC3WhF6Hx24TvZMrhcrkKhUPbqli1bCgsLp06dSuy3n5HYNXv3\n7t374osvNApnzJihkXBcvHjx66+/Vs7xdnV1ff/993v27Kk8pFxLSUk1CVxjV+M2dQtkYD/r\ngX5mBbrRitD5LMJ3smXMnDlz5syZVVVVR48eVY69+/333zt06DBw4EB77WckdvZPJpN99913\nx48fV+4GBwd/9NFHQqFQdYKbm5tqW2P1bdUPt/ovAJgG/cwKdKMVofNZge9ky/Pw8Jg8eXJq\naqpyQZkzZ84MHDjQXvsZiZ2dq6mpWbVqlWrCdkxMzJw5czQGE7i7u1PUf18up7E26ePHj5Ub\nQUFBFonXnqGfWYFutCJ0PnP4Tja3R48ebdu2Tbn93nvv+fn5qQ61bt1amdhVVVUR++1nJHbN\nXkxMTExMjM5Dcrl8w4YNqm+Qxt7n7eDgEBwcnJ+fTwi5ffv2uHHjlOXl5eXFxcXKbeXk8JZM\nTz8bCP3MCnSjFaHzGcJ3sgX4+PjcvXtXuZ2Zmal6jYRMJlP1vHJ9O3vtZ7x5wp6dOHFC+SZv\nQki/fv2io6PL/k75VwshRLWEVXp6+rFjx8RicWlp6Zdffqks5HA4zfF9eTYI/cwKdKMVofOZ\nwHeyBTg7O6vWJd6zZ09qaqpEInn69Onnn3+uej+YclkZYqf9TOmZFQLN3dy5c1XLauvUsWNH\n5YSAqqqq2bNnK1+Zp23EiBEzZ840S4h2p66ubsKECcpt7T/H0c+sQDeaFX6GzQffyZaRkpKy\natWqxo4GBgZu3rzZwcGB2Gk/446d3aqrq9P/DaLOw8Nj0aJFynewaAgNDdW5qCaYAP3MCnSj\nFaHzTYbvZIuJioqaNGmS8r1hGgICApYsWaLM6oid9jN35cqV1o4BzKKwsPDkyZP6z/H29h46\ndKhy29/fPzo6WqFQiEQiiUTi5eUVFhY2fPjwWbNmaQzsBT2kUulvv/2m3I6KitJ4cRBBP7ME\n3Wg++Bk2E3wnW9ILL7zQt2/f+vp6DodTX1/v7e3dpUuX4cOHz58/39vbW/1M++tnPIoFAAAA\nsBN4FAsAAABgJ5DYAQAAANgJJHYAAAAAdgKJHQAAAICdQGIHAAAAYCeQ2AEAAADYCSR2AOZS\nUFBA/Z3O90ICgC1rRr/IpaWle/bsmTBhQu/evdu2bevo6Ojh4dGhQ4eYmJgPP/zw5MmTCoXC\n2jGC2SGxgxYnOTmZatzt27f11L1586aeugcOHLDYpzCHbdu26fl0hvP397f2RwFoWf766683\n3nhDKBROmTJl3759t27devToUUNDQ3V1dV5eXnJy8vr164cNG9axY8fNmzfLZDJrxwtmhMQO\n4G8uX76s5+jVq1ctFgkAgCFWrlwZHh7+22+/NfnGgby8vIULF7744ouZmZmWiQ0sD4kdwN8g\nsQMghCxevFjjRmyTr8MCy5PJZFOmTFm1apVEIjG8Vlpa2ssvv5yWlma+wMCKkNgB/A0SOwBo\nLmbMmLFnzx4TKlZUVAwePLi4uJj1kMDqeNYOAMC2FBYWFhUVBQYGah8qKSkpKCgwvCk+nx8a\nGqpeorNZALBlNvuL/Ouvv+7evVu7nKKo6OjouLi4wMDAhoaG3NzcCxcu3Lp1S+O0srKy2bNn\nJyUlWSJWsCAkdgCaLl++/Oabb2qXG3u7rk2bNvfu3WMpKEsYN25cr169dB7KzMycPn26RmFy\ncrJAINA+mc/nsx8cgJXY5i9yRUXFrFmztMt79+69a9eurl27apQfOnRo9uzZJSUl6oWHDx8+\nf/58TEyM+eIEy0NiB0AIIVwuVy6XK7cNTOzUq5jsypUriYmJ6iVvvPHGkCFDCCEZGRmJiYl/\n/vlncXGxSCRq1apV9+7dX3vttSlTpuhMp5hr3bp169atDT+/T58+hkciFouPHDly9OjRlJSU\np0+f1tTU+Pn5BQQExMXFjRkzpkePHo1VPHLkyOHDh9VLFixYEBERIZFIdu/e/csvv9y/f7+8\nvFwoFHbp0mXy5Mnjx493dHRUnXznzp3du3efPn368ePHdXV1vr6+vXr1Gjt27IQJE3g8HV+A\nP/zwQ3JysnrJ4sWLO3fuLJVKf/vtt8TExOzs7KdPn/r5+bVv337MmDETJ0708/Mz38fXo6qq\natOmTSdPnrx79+7mzZu1M+9bt24pJ0jm5ORUVVXJZLKAgIDAwMCgoKCIiIg333yzffv2JlzX\n8pr8pOboXqNY/hd5586d5eXlGoWDBg06duyYzmZHjx4tFAoHDhyoMRovMTERiZ29oQFamPPn\nz2v/IkRFRam2e/bsqbNiv379VOcEBwe3atVKoxHlrDSVa9euaZwwZcoUjTa1n6Rs2rRJJpN9\n/PHHHI7uIbDBwcEpKSnm6p1GaH8WQohYLDaw+v79+4ODgxv7FiKEjBkzpqCgQGfdlStXapx8\n4sSJe/fudevWTWdTERER9+7do2laIpEsWbKksW6MjIx8+PCh9uW074KcP3++pKSkb9++Ottx\nd3ffuXOn+T6+zp8QmqYvXbrk4+OjKvz222/Va2VnZw8YMEDPFQkhFEVNnTq1rKxM53Xff/99\njfNPnDhhYHfpbPDo0aMaZyYkJDD/pAy71xA2+Issk8m0P7JQKKyqqtJf8ZNPPtGo5eLiIpVK\nTQsDbBMmTwAQQkj//v1V23fu3BGJRBonSCSSlJQU1a56kscumqZnzpy5Zs2axpYSLSgoiImJ\nycnJMVMArFu+fPn48eP1D088ePBgz549DZymd+/evZiYmDt37ug8mpWVFRcX9/z58ylTpnz6\n6aeNdWN6evqQIUMqKiqavFxtbW1sbGxjD+Krq6unT5+ekJDQWHXWPz4hJCUlZfjw4WVlZTqP\npqam9u7d++LFi/oboWk6MTExNjZWvRNUaxlu2rRJ4/xhw4YpD33wwQcGxsmc/k9KzNO9rDDr\nL3JycrL2R/7kk0/c3d31V5w6dSqXy1Uvqa2tffjwoQkxgM1CYgdAyN8TNblcfv36dY0T0tLS\nxGKxajc6OtpMkSQmJu7atUv/OTU1Nf/+97/NFAC7Nm7cuGbNGkPOLCsrGzRokCH/z73//vtP\nnz7Vc0JxcXHXrl1/+ukn/e3k5uauX7++yct9+OGHTQ6x2rhx4/bt23WWs/7x5XL5tGnTqqur\ndR6tra0dNWpUY0e13blzR09Wal36PykxT/eyxay/yNqT9z08PCZOnNhkRaFQ+Oqrrzr+3f37\n902IAWwWEjsAQv5+x47o+t7UuGFjvjt2d+/eVd+lKErnaefPn9f/kgxbkJWVtWLFCo3Ctm3b\nvvXWW0uWLImLi9O4edDYeHAN6kMbG+ufJ0+eqO82dtrOnTubfMmS+q1BgUDg4eGh87SEhASN\ndNNMH/+HH35IT09v7OiXX36pvYaFp6dnjx49YmJiQkNDtbti165dhty5tDz9n9RM3csWs/4i\nX7lyRaPktddeUx9aqseRI0fq/2706NHGBgC2DIkdACGE+Pv7d+jQQbWrP7Fzc3PTnnTGrv79\n+586daq0tLS2tvbmzZvKUdgaTp8+bdYYmFu2bFlDQ4N6yZgxY+7evbtnz56NGzeePn36zJkz\nGjMPzp49e/DgwSZbdnJy2rx5c25ubkNDQ2pqalxcnGmnVVRUZGVlGfJZQkJCjh8/XldXV1lZ\nmZ2dPW7cOI0TqqurN2zYoF5ipo+fkZGh5+iPP/6oUbJu3bqSkpLU1NRz587du3cvKyurXbt2\n6ifI5fJz584pt11cXAIDAwMDA11dXTXaadWqlfJQk8/72KL/k5rvp4tFZvpF1l67pE+fPiaG\nCPbHymP8ACxO5+QJmqbfeust1a67u7tcLlevFRQUpDo6ePBgmqbNNHmCEDJ9+nSFQqF+mkKh\n0F6IZPLkyWbpIF1MmDzx+PFjjWmngYGB2lW0X7A7dOhQ9RO0J08QQv7880/1c2pra3UOnzfk\nNI05ATpv6gQHB5eWlmpEPmXKFI3TfH19VePQ2fr4On9ClAYMGDB79uzPPvvs448/vnTpEk3T\nRUVF+ltT0l7SdvPmzRrn2MLkCT2flK3uNYSt/SLL5XLt+3+NdTu0QLhjB/Bf6k9Xq6ur1e8W\nFBcXP3r0SOeZrBMKhV988YXGFzdFUe+9957GmXpGlNuC3377TeNd40uXLtVeiGHMmDHqU5IJ\nIcqlSfS0PGTIkNjYWPUSZ2fnoUOHmuWWXGYAAApYSURBVHaa9kQZbWvWrPH19dUo/PzzzzUe\nfj179kx168t8H58QEhgYeOrUqQsXLmzbtu39999fvXq18mdSewzZiBEjtKtrr89SWVmp/4rW\n0tgnNWv3ssJ8v8jKqa8ahYasuQMtBBI7gP/SM8zOYgPsCCExMTFubm7a5WFhYRol6pM5bJD2\nBBTtpEpJ4wmpQqHQHkKkTudCHv7+/qad1qRWrVqNHz9eu9zb21t7vUPVMzLzfXxCyP/93/8N\nHjxYu7xHjx63/y4+Pl77NP3vzbMpjX1Ss3YvK8z3i6y9fB0hxGLPx8H2IbED+K+wsDBvb2/V\nbmOJHZfLNetwlvDwcJ3lRi0dbAs0hgG5u7t37NhR55na68eqryyjTeeyutoPpww8rUljxoxp\nbCFZ7XmIqampyg3zffzevXsPHz5c5yEPD49uf6f+/315efmZM2eWLl1qyFxgW6Dnk5qve9li\nvl9k7dt1hJDGVsuDFghvngD4L4qi+vXrd+TIEeWuemKn/id+ZGSkzj/E2eLs7KyzvNl9cWuM\n96qvr1efnqJO+46F/gkNBs7+M/C0Jul5PYPGLARCiGpGqvk+vuETd5R3py5dupSSkpKSkpKX\nl2dgRRuh55Oar3vZYr5fZPW/P1VqampMuBsNdgmJHcD/qCd2BQUFxcXFAQEBDQ0N6kubmvU5\nrN2QSqW1tbUaJfn5+QZWt6nVN/S88V37kHKwmlk/viHvAbt9+/b27duTkpL0L/hn4xr7pPb0\n02UCT09PiqI07ts19w8FLGpm9wAAzErnMLvU1FT1VRWQ2BnC8AVydaqqqmIrEub0DEt3cXHR\nWBakpqaGmPnjOzk56a++YsWKqKioHTt2NOusjjT+Se3pp8sEHA5H+6adxrJ50JIhsQP4n169\neqmPplImdpacOWE3mLzdnBDy/9q795AmuzgO4NswDV0s55tay5Z/WEHo1nW2RmaBjYIMuxCK\nXRhGQtBFhIqIkLBRDFZMywtmEuQYNrJi9Y8oCMqEymphUpJCQXNZgSZOp+8fg73PztnmfGwX\nn/f7+Wv7PcftHHmon+c553fGx8f/Vk/mb3h42N+liYkJYurI/Zg+gsOvrKysqKjwV3VZJBIV\nFxdXVFSw/vxowKW7i52NGzcSkeAPTJuamprwRuwvhoUOiR3Af+Li4rZs2eJ5Syd2K1euZBa0\nA3+EQiFR918ulwdfhymqTsKli8N5DA0NEU/EEhMTeZEb/tevX+myfwkJCQUFBXfv3n39+rXD\n4WhqasrJyWH3+VGCS3cXO/SRhq2trT43VdAOHz5MHCl2+/btEPQRIgaJHYAX5oRcb2/v2NgY\nM7HDdF3wkpKSmG8X3Mp9jwA9py9JJBL3i4gM32g0Tk5OMiMqlerTp08tLS2nT5+Wy+Xuor5D\nQ0Oh64O/9OLPnz9/8Vs4c3exQ/9DNDg46KmhGMD09HRHRwcR9LehGBYoJHYAXpjL7Kampkwm\nE/PkTSR2wZPJZMy3v3//jtoquIGZzWan0+nzUnNzMxHZunWr+0VEhv/x40ciYjAYUlNTiWDw\n+wxYsNvtPuN/dxEYZ+4udnJzc+mNO+Xl5bMefNzV1UVvs1i/fv3f7BxEGhI7AC9KpZJZ6kyn\n0zGvRnNi53Q6f3jzWcg0bOhqf/RUgdv09PRvb1G1Csput5tMJjrucDgePXpEBLdt2+Z+EZHh\nDwwMEJGMjAwiMjMz43M4s3K5XHSQrinT19dHN5uZmbFYLCy+1B/O3F3sxMTE0Ie5vXr1qqys\nLMBPTUxM0OdeZGVlYcaOY5DYAXgRi8XMyqLv37/3vBYKhcQ8QVQxm83/eIvsH+L0YVZardZn\ny5qamqXe7t+/H/oOzsHVq1fpLPn8+fNEjTSpVOpJ7CIyfHq2jE71Hj9+/PbtWxYf7nA46CC9\nPbOmpobOnPR6PX1WxHxw6e5ip6SkhD5tQq/XazQan9t+3717p1arrVYrES8qKgpVFyFCkNgB\nkPxNyykUCmLJNgSQnZ0tl8uZke7u7kuXLhFb8Nra2q5cucKMxMfH00d1RdbAwMD27ds9a5g+\nf/588ODBhw8fEs00Go2n/GxEhk+sPOPxeGfPnvUcRepyuerq6oL8j5z+KIPBYLVa+/v7mbtJ\n6BrC3759y8nJ6erqGh8fHxsbs1qthYWF9ETRPHHp7mInOTnZYDDQ8YaGhvT09FOnTjU2Nr54\n8aK5ufnGjRsFBQUymay9vZ1oLJVKz5w5E47uQhihQDEASaVS1dbW0vFofg4bna5du3bgwAFm\nRKvVPn36VKVSpaWljYyMdHZ20lMIly9fprOKiOvr69u1a5dQKFy8eLHPuSuJRHLu3DlmJPzD\n37RpE7GCvq2tTSqVZmZm8vn83t7e4HcwrF27loj09PS4H4BevHjRcyjZjh074uLimIUe3S09\nSxqC3KrJApfuLnaKi4ufP39uNBqJ+M+fP+vq6urq6gL/uEAgqK6u9ndCBixcSOwASESZYg8k\ndnOVn59//PjxBw8eMIM2my3AmU65ubnl5eWh79ocLFmyxF12mMfjjY6Ojo6O0m34fH51dTVx\n1lz4h19YWKjT6YhcamxsrLu7mxmJjY0ltoPQC+qVSuXSpUtn3ZEgFouLiooaGhroS0Q3hEKh\nz18da9y4u+apqalpenqaxaJJPp9fX1/v7yheWNDwKBaAlJ6evmLFCiIoEAiys7Mj0p8Fraam\npqCgIMjGeXl5ZrM5NjY2pF2aq1u3biUkJARuU1VVtX//fjoe5uFv2LChpKQkcBupVOo5N8+D\nqMLN4/FSUlKqqqqCOdj05s2bsxZ3zMjICEWxNA7cXfMUGxvb3Nx84cKFOa0SSUlJMZvNJ0+e\nDF3HIIKQ2AH4QE/aZWZm0kuVYVZxcXEmk0mr1YpEogDNli1bdufOHYvFErhZRKxbt661tdXf\nwWKpqalms7m0tNTn1fAPv6qqSqPR+LtaWFhotVp37txJTC5ardbKykq6sc1mO3HihEwmE4vF\nMTExIpFIIpEQjzKTkpI6OjqysrL8falare7o6EhJSWE1oEA4cHfNn0Ag0Ol0b968UavVszYW\ni8VlZWU2my0/Pz8MfYOIIA8SBgAIhZGREZPJZLFYbDab3W6fnJxctWqVVCpdvXp1Xl7e3r17\nZz0CNTxKS0vv3bvHjLS3t+fk5Njt9vr6+idPnnz58uXXr1/Jyclr1qw5cuTI0aNHg0kXwjz8\nzs7OxsbGDx8+9Pf3O53OtLS03bt3Hzt2bPPmze4GtbW1xCydRCK5fv066290uVxGo7GlpaWn\np2d4eHjRokXLly9XKBRFRUV79uzh8Xi9vb16vZ75I/v27Tt06BDrb2RaKHdXqA0NDT179uzl\ny5eDg4Pfv3//8eNHfHx8YmKiRCJRKBRKpfL/86v4P0NiBwDwH3+JXaT6AwAwJ3gUCwAAAMAR\nSOwAAAAAOAKJHQAAAABHILEDAAAA4AgkdgAAAAAcgcQOAAAAgCOQ2AEAAABwBBI7AAAAAI5A\ngWIAAAAAjsCMHQAAAABHILEDAAAA4AgkdgAAAAAc8S9ym0OKtou5CwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot binned FE regression (REAN2)\n",
    "#load saved model output\n",
    "dt <- read.csv(file=\"model_data/SIFig4_binned_tmin_sleep_duration_rean2_model_output.csv\",header=T)\n",
    "flex.plot <- binned.plot(felm.est = dt,\n",
    "                 plotvar = \"CUTTMIN_rean2\",\n",
    "                 breaks = 5,\n",
    "                 omit = c(5,10), \n",
    "                 linecolor = \"purple\",\n",
    "                 pointfill = \"purple\",\n",
    "                 errorfill = \"purple\",\n",
    "                 xlabel = \"Min. Temperature in C\",\n",
    "                 ylabel = \"Change in Sleep Duration (Minutes)\"\n",
    "                 )\n",
    "# hist <- axis_canvas(flex.plot, axis = \"x\") + \n",
    "#         geom_histogram(data = Climate_Controls_DF, aes(x = Climate_Controls_DF$tmin_rean2), bins=41, fill='gray60', color='gray60', alpha=0.75, size=0.1)\n",
    "# flex.plot <- insert_xaxis_grob(flex.plot, hist, position = \"bottom\", height = grid::unit(0.1, \"null\"))\n",
    "flex.plot <- ggdraw(flex.plot)\n",
    "#ggsave(\"flexplot_binned_REAN2.pdf\")\n",
    "flex.plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{Binned Nighttime Minimum Temperature and Sleep Duration FE Regressions} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{1}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-2} \n",
      " & Binned Rean2 \\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " CUTTMIN\\_rean2(-Inf,-20] & 4.298$^{***}$ \\\\ \n",
      "  & (0.809) \\\\ \n",
      "  CUTTMIN\\_rean2(-20,-15] & 3.404$^{***}$ \\\\ \n",
      "  & (0.548) \\\\ \n",
      "  CUTTMIN\\_rean2(-15,-10] & 1.508$^{***}$ \\\\ \n",
      "  & (0.418) \\\\ \n",
      "  CUTTMIN\\_rean2(-10,-5] & 1.401$^{***}$ \\\\ \n",
      "  & (0.318) \\\\ \n",
      "  CUTTMIN\\_rean2(-5,0] & 0.727$^{***}$ \\\\ \n",
      "  & (0.259) \\\\ \n",
      "  CUTTMIN\\_rean2(0,5] & 0.644$^{***}$ \\\\ \n",
      "  & (0.164) \\\\ \n",
      "  CUTTMIN\\_rean2(10,15] & $-$1.061$^{***}$ \\\\ \n",
      "  & (0.229) \\\\ \n",
      "  CUTTMIN\\_rean2(15,20] & $-$3.650$^{***}$ \\\\ \n",
      "  & (0.257) \\\\ \n",
      "  CUTTMIN\\_rean2(20,25] & $-$6.176$^{***}$ \\\\ \n",
      "  & (0.456) \\\\ \n",
      "  CUTTMIN\\_rean2(25,30] & $-$8.666$^{***}$ \\\\ \n",
      "  & (0.606) \\\\ \n",
      "  CUTTMIN\\_rean2(30, Inf] & $-$11.536$^{***}$ \\\\ \n",
      "  & (1.380) \\\\ \n",
      "  tmin\\_norm\\_7\\_rean2 & $-$0.046 \\\\ \n",
      "  & (0.061) \\\\ \n",
      "  CUTPRCP\\_rean2(0,1] & 0.087 \\\\ \n",
      "  & (0.101) \\\\ \n",
      "  CUTPRCP\\_rean2(1,2] & 0.489$^{**}$ \\\\ \n",
      "  & (0.233) \\\\ \n",
      "  CUTPRCP\\_rean2(2,3] & 0.951$^{***}$ \\\\ \n",
      "  & (0.338) \\\\ \n",
      "  CUTPRCP\\_rean2(3, Inf] & 1.325$^{***}$ \\\\ \n",
      "  & (0.412) \\\\ \n",
      "  prcp\\_norm\\_7\\_rean2 & 0.039 \\\\ \n",
      "  & (1.087) \\\\ \n",
      "  CUTTRANGE\\_rean2(-Inf,0] & $-$0.959 \\\\ \n",
      "  & (1.149) \\\\ \n",
      "  CUTTRANGE\\_rean2(0,5] & 0.515$^{***}$ \\\\ \n",
      "  & (0.138) \\\\ \n",
      "  CUTTRANGE\\_rean2(10,15] & $-$0.766$^{***}$ \\\\ \n",
      "  & (0.143) \\\\ \n",
      "  CUTTRANGE\\_rean2(15,20] & $-$1.921$^{***}$ \\\\ \n",
      "  & (0.274) \\\\ \n",
      "  CUTTRANGE\\_rean2(20, Inf] & $-$2.702$^{***}$ \\\\ \n",
      "  & (0.815) \\\\ \n",
      "  trange\\_norm\\_7\\_rean2 & $-$0.167$^{**}$ \\\\ \n",
      "  & (0.082) \\\\ \n",
      "  CUTCLOUD\\_rean2(0,20] & 0.658$^{**}$ \\\\ \n",
      "  & (0.327) \\\\ \n",
      "  CUTCLOUD\\_rean2(20,40] & 0.707$^{**}$ \\\\ \n",
      "  & (0.344) \\\\ \n",
      "  CUTCLOUD\\_rean2(40,60] & 1.063$^{***}$ \\\\ \n",
      "  & (0.310) \\\\ \n",
      "  CUTCLOUD\\_rean2(60,80] & 1.414$^{***}$ \\\\ \n",
      "  & (0.328) \\\\ \n",
      "  CUTCLOUD\\_rean2(80,100] & 2.164$^{***}$ \\\\ \n",
      "  & (0.343) \\\\ \n",
      "  cloud\\_norm\\_7\\_rean2 & $-$0.028 \\\\ \n",
      "  & (0.023) \\\\ \n",
      "  CUTRHUM\\_rean2(0,20] & $-$2.880$^{***}$ \\\\ \n",
      "  & (0.889) \\\\ \n",
      "  CUTRHUM\\_rean2(20,40] & $-$1.537$^{***}$ \\\\ \n",
      "  & (0.415) \\\\ \n",
      "  CUTRHUM\\_rean2(40,60] & $-$0.146 \\\\ \n",
      "  & (0.154) \\\\ \n",
      "  CUTRHUM\\_rean2(80,100] & $-$0.079 \\\\ \n",
      "  & (0.131) \\\\ \n",
      "  rhum\\_norm\\_7\\_rean2 & 0.078$^{***}$ \\\\ \n",
      "  & (0.029) \\\\ \n",
      "  CUTWIND\\_rean2(5,10] & 0.306$^{***}$ \\\\ \n",
      "  & (0.094) \\\\ \n",
      "  CUTWIND\\_rean2(10,Inf] & 0.430$^{**}$ \\\\ \n",
      "  & (0.184) \\\\ \n",
      "  wind\\_norm\\_7\\_rean2 & $-$0.174 \\\\ \n",
      "  & (0.165) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes \\\\ \n",
      "Date FE & Yes \\\\ \n",
      "State:Month FE & Yes \\\\ \n",
      "Observations & 7,174,612 \\\\ \n",
      "R$^{2}$ & 0.276 \\\\ \n",
      "Adjusted R$^{2}$ & 0.269 \\\\ \n",
      "Residual Std. Error & 77.937 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{1}{r}{Standard errors are in parentheses and are clustered on the first admin level} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# invisible(stargazer(flex_t_binned_rean2,\n",
    "#           type='latex',\n",
    "#           title = \"Binned Nighttime Minimum Temperature and Sleep Duration FE Regressions\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Binned Rean2'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the first admin level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Supplementary Figure 5: WHO BMI Category Subgroup Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmin + tmin:BMI_Lab +      tmin_norm_w7 + prcp + prcp_norm_w7 + trange + trange_norm_w7 +      cloud_rean2 + cloud_norm_7_rean2 + rhum_rean2 + rhum_norm_7_rean2 +      wind_rean2 + wind_norm_7_rean2 | userID + unique_day_no +      stateyrmn | 0 | state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-384.20  -49.09   -2.29   45.73  406.65 \n",
       "\n",
       "Coefficients:\n",
       "                         Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin                    -0.294944     0.029845  -9.882  < 2e-16 ***\n",
       "tmin_norm_w7            -0.168450     0.088752  -1.898 0.057697 .  \n",
       "prcp                     0.470336     0.130298   3.610 0.000307 ***\n",
       "prcp_norm_w7             2.356997     1.130229   2.085 0.037032 *  \n",
       "trange                  -0.198885     0.022531  -8.827  < 2e-16 ***\n",
       "trange_norm_w7           0.093752     0.144709   0.648 0.517074    \n",
       "cloud_rean2              0.009380     0.002462   3.810 0.000139 ***\n",
       "cloud_norm_7_rean2      -0.037886     0.025353  -1.494 0.135089    \n",
       "rhum_rean2               0.003463     0.007559   0.458 0.646809    \n",
       "rhum_norm_7_rean2        0.103040     0.039463   2.611 0.009027 ** \n",
       "wind_rean2               0.145600     0.021379   6.810 9.73e-12 ***\n",
       "wind_norm_7_rean2       -0.333620     0.204716  -1.630 0.103171    \n",
       "tmin:BMI_Lab2overweight  0.009111     0.018834   0.484 0.628570    \n",
       "tmin:BMI_Lab3obese       0.013948     0.028446   0.490 0.623901    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.65 on 3812911 degrees of freedom\n",
       "  (3545242 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2829   Adjusted R-squared: 0.273 \n",
       "Multiple R-squared(proj model): 0.0001524   Adjusted R-squared: -0.0137 \n",
       "F-statistic(full model, *iid*):28.48 on 52817 and 3812911 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 37.01 on 14 and 1046 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #prepare BMI interaction model\n",
    "# Climate_Controls_DF$BMI_Lab <- factor(Climate_Controls_DF$BMI_Lab)\n",
    "# Climate_Controls_DF$BMI_Lab <- relevel(Climate_Controls_DF$BMI_Lab, ref=\"1normal\")\n",
    "# levels(Climate_Controls_DF$BMI_Lab)\n",
    "\n",
    "# #WHO BMI Category Subgroup Analysis\n",
    "# linear_t_BMI <- felm(formula = sleep_duration_minutes ~ tmin + tmin:BMI_Lab + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "# summary(linear_t_BMI)\n",
    "# #save model coefficients for plotting\n",
    "# dt <- as.data.frame(summary(linear_t_BMI)$coefficients[, 1:4]) # extract from felm summary (use df to keep coefficient\n",
    "# write.csv(dt,file=\"model_data/SIFig_5_linear_tmin_subgroup_by_BMI.csv\")#save data as CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tOmzSWXXBJF0cKFCz///PObb775wQcf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\nAokdAAAAgJ74F5wj2YzXKvabAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#load dt\n",
    "dt <- read.csv(file=\"model_data/SIFig_5_linear_tmin_subgroup_by_BMI.csv\",header=T)\n",
    "#Plot tmin results\n",
    "dt <- dt[grepl(\"tmin\", dt$X), ] # extract variable name (e.g., tmax.cut, ppt.cut, ...), limit to matches\n",
    "dt <- dt[!grepl(\"norm\", dt$X), ] #deselect norm variables\n",
    "dt <- as.data.table(dt)\n",
    "#recover marginal effects \n",
    "base<-dt[X==\"tmin\",.(Estimate)]\n",
    "dt[,base:=base]\n",
    "base<-dt[X==\"tmin\",.(Estimate)]\n",
    "dt[,plotcoef:=Estimate]\n",
    "dt[X!=\"tmin\",plotcoef:=plotcoef+base]\n",
    "dt[,plotcoef:=abs(plotcoef)] #plot absolute sleep loss\n",
    "#specify names for plotting\n",
    "dt[X==\"tmin\",X:=\"1normal\"]\n",
    "dt[X==\"tmin:BMI_Lab2overweight\",X:=\"2overweight\"]\n",
    "dt[X==\"tmin:BMI_Lab3obese\",X:=\"3obese\"]\n",
    "setnames(dt,\"X\",\"Subgroups\")\n",
    "dt[,Subgroups:=as.factor(Subgroups)]\n",
    "barplot<-ggplot(dt) +\n",
    "  geom_bar(aes(x=Subgroups, y=plotcoef), stat=\"identity\", fill=\"#ffb600\", alpha=1) +\n",
    "  geom_errorbar(aes(x=Subgroups, ymin=plotcoef-Cluster.s.e.*1.96, ymax=plotcoef+Cluster.s.e.*1.96), width=0, alpha=0.9, size=1) +\n",
    "  scale_y_continuous(breaks=seq(0,1.5,0.5),limits=c(0,1.5),labels = scales::number_format(accuracy = 0.01)) +\n",
    "  theme_classic() +\n",
    "  ggtitle(\"Marginal Effect of Tmin by BMI\")\n",
    "barplot\n",
    "#ggsave(\"barplot_marginal_effect_by_BMI.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{Subgroup Analysis: Nighttime Minimum Temperature and Sleep BMI Category Interaction Model} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{1}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-2} \n",
      " & Base Level: Normal BMI Range \\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " TMIN & $-$0.295$^{***}$ \\\\ \n",
      "  & (0.030) \\\\ \n",
      "  TMIN.NORM & $-$0.168$^{*}$ \\\\ \n",
      "  & (0.089) \\\\ \n",
      "  PRCP & 0.470$^{***}$ \\\\ \n",
      "  & (0.130) \\\\ \n",
      "  PRCP.NORM & 2.357$^{**}$ \\\\ \n",
      "  & (1.130) \\\\ \n",
      "  TRANGE & $-$0.199$^{***}$ \\\\ \n",
      "  & (0.023) \\\\ \n",
      "  TRANGE.NORM & 0.094 \\\\ \n",
      "  & (0.145) \\\\ \n",
      "  CLOUD & 0.009$^{***}$ \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  CLOUD.NORM & $-$0.038 \\\\ \n",
      "  & (0.025) \\\\ \n",
      "  HUMID & 0.003 \\\\ \n",
      "  & (0.008) \\\\ \n",
      "  HUMID.NORM & 0.103$^{***}$ \\\\ \n",
      "  & (0.039) \\\\ \n",
      "  WIND & 0.146$^{***}$ \\\\ \n",
      "  & (0.021) \\\\ \n",
      "  WIND.NORM & $-$0.334 \\\\ \n",
      "  & (0.205) \\\\ \n",
      "  TMIN:BMI_Overweight & 0.009 \\\\ \n",
      "  & (0.019) \\\\ \n",
      "  TMIN:BMI_Obese & 0.014 \\\\ \n",
      "  & (0.028) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes \\\\ \n",
      "Date FE & Yes \\\\ \n",
      "State:Month FE & Yes \\\\ \n",
      "Observations & 3,865,729 \\\\ \n",
      "R$^{2}$ & 0.283 \\\\ \n",
      "Adjusted R$^{2}$ & 0.273 \\\\ \n",
      "Residual Std. Error & 77.646 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{1}{r}{Standard errors are in parentheses and are clustered on the state admin level.} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# #BMI interaction model\n",
    "# invisible(stargazer(linear_t_BMI,\n",
    "#           type='latex',\n",
    "#           title = \"Subgroup Analysis: Nighttime Minimum Temperature and Sleep BMI Category Interaction Model\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Base Level: Normal BMI Range'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           covariate.labels = c(\"TMIN\", \"TMIN.NORM\", \"PRCP\", \"PRCP.NORM\", \"TRANGE\", \"TRANGE.NORM\", \"CLOUD\", \"CLOUD.NORM\", \"HUMID\", \"HUMID.NORM\", \"WIND\", \"WIND.NORM\", \"TMIN:BMI_Overweight\", \"TMIN:BMI_Obese\"),\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the state admin level.'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#See Projection Plot .RMD file "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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f0MTGZmZqoLaRtStX90OBwejyfWUJ1/eSLWnjUQCMQZigbUk7WZXTaZphkIBBwO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\nRK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #Supplementary Figure 7: Histogram of total nighttime sleep observations\n",
    "# ## plot distribution of observations\n",
    "\n",
    "# dist[,Total_Nighttime_Sleep_Records:=tot]\n",
    "# dist_plot<-ggplot(dist, aes(x=Total_Nighttime_Sleep_Records)) +\n",
    "#     geom_histogram(binwidth=10, fill=\"#69b3a2\", color=\"#e9ecef\", alpha=0.9) +\n",
    "#     ggtitle(\"Histogram of # of Subject-level Nighttime Sleep Records\") +\n",
    "#     theme_minimal() +\n",
    "#     theme(\n",
    "#       plot.title = element_text(size=15)\n",
    "#     )\n",
    "\n",
    "# save_plot(\"obs_count_histogram_plot.pdf\", dist_plot)\n",
    "# dist_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Additional Robustness Checks & Sensitivity Analyses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Linear Nighttime Minimum Temperature and Sleep Duration FE Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmin + tmin_norm_w7 +      prcp + prcp_norm_w7 + trange + trange_norm_w7 + cloud_rean2 +      cloud_norm_7_rean2 + rhum_rean2 + rhum_norm_7_rean2 + wind_rean2 +      wind_norm_7_rean2 | userID + unique_day_no + stateyrmn |      0 | state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-384.35  -49.13   -2.32   45.76  407.67 \n",
       "\n",
       "Coefficients:\n",
       "                    Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin               -0.294272     0.026794 -10.983  < 2e-16 ***\n",
       "tmin_norm_w7       -0.164135     0.079032  -2.077  0.03782 *  \n",
       "prcp                0.482288     0.120260   4.010 6.06e-05 ***\n",
       "prcp_norm_w7        2.250506     1.132239   1.988  0.04685 *  \n",
       "trange             -0.207180     0.022562  -9.183  < 2e-16 ***\n",
       "trange_norm_w7      0.037610     0.144961   0.259  0.79529    \n",
       "cloud_rean2         0.010141     0.002375   4.270 1.96e-05 ***\n",
       "cloud_norm_7_rean2 -0.041776     0.023876  -1.750  0.08017 .  \n",
       "rhum_rean2          0.000686     0.007501   0.091  0.92713    \n",
       "rhum_norm_7_rean2   0.099271     0.038372   2.587  0.00968 ** \n",
       "wind_rean2          0.127602     0.021990   5.803 6.53e-09 ***\n",
       "wind_norm_7_rean2  -0.270277     0.192921  -1.401  0.16122    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.72 on 4353298 degrees of freedom\n",
       "  (3063777 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2842   Adjusted R-squared: 0.2742 \n",
       "Multiple R-squared(proj model): 0.0001518   Adjusted R-squared: -0.01376 \n",
       "F-statistic(full model, *iid*):28.53 on 60563 and 4353298 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 42.96 on 12 and 1193 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #Linear tmin specification\n",
    "# linear_t <- felm(formula = sleep_duration_minutes ~ tmin + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "# summary(linear_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_24hr_minutes ~ tmin + tmin_norm_w7 +      prcp + prcp_norm_w7 + trange + trange_norm_w7 + cloud_rean2 +      cloud_norm_7_rean2 + rhum_rean2 + rhum_norm_7_rean2 + wind_rean2 +      wind_norm_7_rean2 | userID + unique_day_no + stateyrmn |      0 | state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-406.83  -52.20   -3.61   47.40  536.99 \n",
       "\n",
       "Coefficients:\n",
       "                     Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin               -0.3185555    0.0286583 -11.116  < 2e-16 ***\n",
       "tmin_norm_w7       -0.0614044    0.0912391  -0.673 0.500944    \n",
       "prcp                0.4627255    0.1338007   3.458 0.000544 ***\n",
       "prcp_norm_w7        1.9601242    1.1723847   1.672 0.094542 .  \n",
       "trange             -0.2433723    0.0245155  -9.927  < 2e-16 ***\n",
       "trange_norm_w7      0.1513142    0.1635094   0.925 0.354750    \n",
       "cloud_rean2         0.0110951    0.0028341   3.915 9.04e-05 ***\n",
       "cloud_norm_7_rean2 -0.0532743    0.0264688  -2.013 0.044144 *  \n",
       "rhum_rean2          0.0004187    0.0079584   0.053 0.958040    \n",
       "rhum_norm_7_rean2   0.0958239    0.0395059   2.426 0.015285 *  \n",
       "wind_rean2          0.1503711    0.0258452   5.818 5.95e-09 ***\n",
       "wind_norm_7_rean2  -0.2618275    0.2196276  -1.192 0.233205    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 82.5 on 4345352 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2746   Adjusted R-squared: 0.2646 \n",
       "Multiple R-squared(proj model): 0.0001533   Adjusted R-squared: -0.01361 \n",
       "F-statistic(full model, *iid*):27.49 on 59818 and 4345352 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 44.35 on 12 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #linear FE 24hr sleep dv specification \n",
    "# linear_t_24hr <- felm(formula = sleep_duration_24hr_minutes ~ tmin + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(linear_t_24hr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmin_rean2 + tmin_norm_7_rean2 +      prcp_rean2 + prcp_norm_7_rean2 + trange_rean2 + trange_norm_7_rean2 +      cloud_rean2 + cloud_norm_7_rean2 + rhum_rean2 + rhum_norm_7_rean2 +      wind_rean2 + wind_norm_7_rean2 | userID + unique_day_no +      stateyrmn | 0 | state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-383.73  -49.17   -2.05   46.20  403.04 \n",
       "\n",
       "Coefficients:\n",
       "                     Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin_rean2          -0.283653     0.019952 -14.217  < 2e-16 ***\n",
       "tmin_norm_7_rean2    0.005224     0.071268   0.073  0.94157    \n",
       "prcp_rean2           0.294669     0.073610   4.003 6.25e-05 ***\n",
       "prcp_norm_7_rean2   -0.103691     0.978487  -0.106  0.91561    \n",
       "trange_rean2        -0.236790     0.022950 -10.318  < 2e-16 ***\n",
       "trange_norm_7_rean2  0.024982     0.083124   0.301  0.76376    \n",
       "cloud_rean2          0.015649     0.001852   8.451  < 2e-16 ***\n",
       "cloud_norm_7_rean2  -0.028752     0.022370  -1.285  0.19869    \n",
       "rhum_rean2           0.007727     0.005348   1.445  0.14847    \n",
       "rhum_norm_7_rean2    0.092467     0.030861   2.996  0.00273 ** \n",
       "wind_rean2           0.098986     0.016284   6.079 1.21e-09 ***\n",
       "wind_norm_7_rean2   -0.058627     0.143314  -0.409  0.68248    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.94 on 7105705 degrees of freedom\n",
       "  (236359 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2758   Adjusted R-squared: 0.2688 \n",
       "Multiple R-squared(proj model): 0.0001423   Adjusted R-squared: -0.009554 \n",
       "F-statistic(full model, *iid*):39.27 on 68906 and 7105705 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 59.38 on 12 and 1256 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #linear FE REAN2 specification\n",
    "# linear_t_rean2 <- felm(formula = sleep_duration_minutes ~ tmin_rean2 + tmin_norm_7_rean2 + \n",
    "#                                           prcp_rean2 + prcp_norm_7_rean2 + \n",
    "#                                           trange_rean2 + trange_norm_7_rean2 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state,\n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(linear_t_rean2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{S1. Main Effects: Nighttime minimum temperature and sleep duration FE regressions} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lccc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{3}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-4} \n",
      " & Linear TMIN GHCND & Linear TMIN Reanalysis 2 & Linear TMIN GHCND 24hr \\\\ \n",
      "\\\\[-1.8ex] & (1) & (2) & (3)\\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " TMIN & $-$0.295$^{***}$ &  & $-$0.319$^{***}$ \\\\ \n",
      "  & (0.027) &  & (0.029) \\\\ \n",
      "  TMIN.NORM & $-$0.178$^{**}$ &  & $-$0.061 \\\\ \n",
      "  & (0.085) &  & (0.091) \\\\ \n",
      "  PRCP & 0.484$^{***}$ &  & 0.463$^{***}$ \\\\ \n",
      "  & (0.120) &  & (0.134) \\\\ \n",
      "  PRCP.NORM & 2.118$^{*}$ &  & 1.960$^{*}$ \\\\ \n",
      "  & (1.085) &  & (1.172) \\\\ \n",
      "  TRANGE & $-$0.208$^{***}$ &  & $-$0.243$^{***}$ \\\\ \n",
      "  & (0.023) &  & (0.025) \\\\ \n",
      "  TRANGE.NORM & 0.055 &  & 0.151 \\\\ \n",
      "  & (0.147) &  & (0.164) \\\\ \n",
      "  CLOUD &  & $-$0.284$^{***}$ &  \\\\ \n",
      "  &  & (0.020) &  \\\\ \n",
      "  CLOUD.NORM &  & 0.005 &  \\\\ \n",
      "  &  & (0.071) &  \\\\ \n",
      "  HUMID &  & 0.295$^{***}$ &  \\\\ \n",
      "  &  & (0.074) &  \\\\ \n",
      "  HUMID.NORM &  & $-$0.104 &  \\\\ \n",
      "  &  & (0.978) &  \\\\ \n",
      "  WIND &  & $-$0.237$^{***}$ &  \\\\ \n",
      "  &  & (0.023) &  \\\\ \n",
      "  WIND.NORM &  & 0.025 &  \\\\ \n",
      "  &  & (0.083) &  \\\\ \n",
      "  cloud\\_rean2 & 0.010$^{***}$ & 0.016$^{***}$ & 0.011$^{***}$ \\\\ \n",
      "  & (0.002) & (0.002) & (0.003) \\\\ \n",
      "  cloud\\_norm\\_7\\_rean2 & $-$0.039 & $-$0.029 & $-$0.053$^{**}$ \\\\ \n",
      "  & (0.024) & (0.022) & (0.026) \\\\ \n",
      "  rhum\\_rean2 & 0.001 & 0.008 & 0.0004 \\\\ \n",
      "  & (0.008) & (0.005) & (0.008) \\\\ \n",
      "  rhum\\_norm\\_7\\_rean2 & 0.104$^{***}$ & 0.092$^{***}$ & 0.096$^{**}$ \\\\ \n",
      "  & (0.040) & (0.031) & (0.040) \\\\ \n",
      "  wind\\_rean2 & 0.128$^{***}$ & 0.099$^{***}$ & 0.150$^{***}$ \\\\ \n",
      "  & (0.022) & (0.016) & (0.026) \\\\ \n",
      "  wind\\_norm\\_7\\_rean2 & $-$0.296 & $-$0.059 & $-$0.262 \\\\ \n",
      "  & (0.188) & (0.143) & (0.220) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes & Yes & Yes \\\\ \n",
      "Date FE & Yes & Yes & Yes \\\\ \n",
      "State:Month FE & Yes & Yes & Yes \\\\ \n",
      "Observations & 4,405,171 & 7,174,612 & 4,405,171 \\\\ \n",
      "R$^{2}$ & 0.284 & 0.276 & 0.275 \\\\ \n",
      "Adjusted R$^{2}$ & 0.274 & 0.269 & 0.265 \\\\ \n",
      "Residual Std. Error & 77.708 & 77.938 & 82.496 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{3}{r}{Standard errors are in parentheses and are clustered on the state admin level} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# invisible(stargazer(linear_t, linear_t_rean2, linear_t_24hr,\n",
    "#           type='latex',\n",
    "#           title = \"S1. Main Effects: Nighttime minimum temperature and change in sleep duration (linear models)\",\n",
    "#           out='linear_tmin.tex',\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Linear TMIN GHCND', 'Linear TMIN Reanalysis 2', 'Linear TMIN GHCND 24hr'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           covariate.labels = c(\"TMIN\", \"TMIN.NORM\", \"PRCP\", \"PRCP.NORM\", \"TRANGE\", \"TRANGE.NORM\", \"CLOUD\", \"CLOUD.NORM\", \"HUMID\", \"HUMID.NORM\", \"WIND\", \"WIND.NORM\"),\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the state admin level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{S1B. Main Effects: Nighttime minimum temperature and change in sleep duration (linear models)} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{1}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-2} \n",
      " & Linear TMIN Reanalysis 2 \\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " TMIN & $-$0.284$^{***}$ \\\\ \n",
      "  & (0.020) \\\\ \n",
      "  TMIN.NORM & 0.005 \\\\ \n",
      "  & (0.071) \\\\ \n",
      "  PRCP & 0.295$^{***}$ \\\\ \n",
      "  & (0.074) \\\\ \n",
      "  PRCP.NORM & $-$0.104 \\\\ \n",
      "  & (0.978) \\\\ \n",
      "  TRANGE & $-$0.237$^{***}$ \\\\ \n",
      "  & (0.023) \\\\ \n",
      "  TRANGE.NORM & 0.025 \\\\ \n",
      "  & (0.083) \\\\ \n",
      "  CLOUD & 0.016$^{***}$ \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  CLOUD.NORM & $-$0.029 \\\\ \n",
      "  & (0.022) \\\\ \n",
      "  HUMID & 0.008 \\\\ \n",
      "  & (0.005) \\\\ \n",
      "  HUMID.NORM & 0.092$^{***}$ \\\\ \n",
      "  & (0.031) \\\\ \n",
      "  WIND & 0.099$^{***}$ \\\\ \n",
      "  & (0.016) \\\\ \n",
      "  WIND.NORM & $-$0.059 \\\\ \n",
      "  & (0.143) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes \\\\ \n",
      "Date FE & Yes \\\\ \n",
      "State:Month FE & Yes \\\\ \n",
      "Observations & 7,174,612 \\\\ \n",
      "R$^{2}$ & 0.276 \\\\ \n",
      "Adjusted R$^{2}$ & 0.269 \\\\ \n",
      "Residual Std. Error & 77.938 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{1}{r}{Standard errors are in parentheses and are clustered on the state admin level} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# invisible(stargazer(linear_t_rean2,\n",
    "#           type='latex',\n",
    "#           title = \"S1B. Main Effects: Nighttime minimum temperature and change in sleep duration (linear models)\",\n",
    "#           out='linear_tmin_rean2.tex',\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Linear TMIN Reanalysis 2'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           covariate.labels = c(\"TMIN\", \"TMIN.NORM\", \"PRCP\", \"PRCP.NORM\", \"TRANGE\", \"TRANGE.NORM\", \"CLOUD\", \"CLOUD.NORM\", \"HUMID\", \"HUMID.NORM\", \"WIND\", \"WIND.NORM\"),\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the state admin level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Nighttime Minimum Temperature and Short Sleep Linear Probability Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = short_7hr_sleep ~ tmin + tmin_norm_w7 + prcp +      prcp_norm_w7 + trange + trange_norm_w7 + cloud_rean2 + cloud_norm_7_rean2 +      rhum_rean2 + rhum_norm_7_rean2 + wind_rean2 + wind_norm_7_rean2 |      userID + unique_day_no + stateyrmn | 0 | state, data = Climate_Controls_DF,      na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "     Min       1Q   Median       3Q      Max \n",
       "-1.33279 -0.37425  0.04266  0.35734  1.29638 \n",
       "\n",
       "Coefficients:\n",
       "                     Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin                1.281e-03    1.224e-04  10.466  < 2e-16 ***\n",
       "tmin_norm_w7        9.519e-04    4.747e-04   2.005 0.044927 *  \n",
       "prcp               -1.560e-03    6.231e-04  -2.504 0.012275 *  \n",
       "prcp_norm_w7       -5.475e-03    5.337e-03  -1.026 0.304949    \n",
       "trange              9.702e-04    1.151e-04   8.426  < 2e-16 ***\n",
       "trange_norm_w7     -5.270e-04    8.450e-04  -0.624 0.532831    \n",
       "cloud_rean2        -3.957e-05    1.204e-05  -3.286 0.001015 ** \n",
       "cloud_norm_7_rean2  1.613e-04    1.083e-04   1.489 0.136577    \n",
       "rhum_rean2          1.741e-05    3.607e-05   0.483 0.629269    \n",
       "rhum_norm_7_rean2  -6.747e-04    1.933e-04  -3.491 0.000482 ***\n",
       "wind_rean2         -4.815e-04    1.256e-04  -3.832 0.000127 ***\n",
       "wind_norm_7_rean2   1.536e-03    7.936e-04   1.935 0.052998 .  \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 0.44 on 4345352 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2356   Adjusted R-squared: 0.2251 \n",
       "Multiple R-squared(proj model): 8.852e-05   Adjusted R-squared: -0.01368 \n",
       "F-statistic(full model, *iid*):22.39 on 59818 and 4345352 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 26.71 on 12 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #shortsleep<7hr\n",
    "# linear_prob_7hr <- felm(formula = short_7hr_sleep ~ tmin + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(linear_prob_7hr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = short_6hr_sleep ~ tmin + tmin_norm_w7 + prcp +      prcp_norm_w7 + trange + trange_norm_w7 + cloud_rean2 + cloud_norm_7_rean2 +      rhum_rean2 + rhum_norm_7_rean2 + wind_rean2 + wind_norm_7_rean2 |      userID + unique_day_no + stateyrmn | 0 | state, data = Climate_Controls_DF,      na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-1.0324 -0.2536 -0.1096  0.2601  1.2547 \n",
       "\n",
       "Coefficients:\n",
       "                     Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin                9.146e-04    9.902e-05   9.236  < 2e-16 ***\n",
       "tmin_norm_w7        2.456e-04    3.255e-04   0.754 0.450558    \n",
       "prcp               -1.477e-03    4.012e-04  -3.682 0.000231 ***\n",
       "prcp_norm_w7        8.044e-03    4.893e-03   1.644 0.100188    \n",
       "trange              4.532e-04    1.056e-04   4.294 1.76e-05 ***\n",
       "trange_norm_w7     -2.749e-04    5.702e-04  -0.482 0.629690    \n",
       "cloud_rean2        -2.371e-05    1.032e-05  -2.297 0.021644 *  \n",
       "cloud_norm_7_rean2  2.052e-04    1.230e-04   1.669 0.095115 .  \n",
       "rhum_rean2         -3.057e-06    2.759e-05  -0.111 0.911769    \n",
       "rhum_norm_7_rean2  -3.916e-04    1.845e-04  -2.122 0.033854 *  \n",
       "wind_rean2         -1.752e-05    9.557e-05  -0.183 0.854501    \n",
       "wind_norm_7_rean2  -1.746e-04    6.439e-04  -0.271 0.786305    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 0.3982 on 4345352 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2012   Adjusted R-squared: 0.1902 \n",
       "Multiple R-squared(proj model): 4.018e-05   Adjusted R-squared: -0.01373 \n",
       "F-statistic(full model, *iid*): 18.3 on 59818 and 4345352 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 16.45 on 12 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #shortsleep<6hr\n",
    "# linear_prob_6hr <- felm(formula = short_6hr_sleep ~ tmin + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(linear_prob_6hr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = short_5hr_sleep ~ tmin + tmin_norm_w7 + prcp +      prcp_norm_w7 + trange + trange_norm_w7 + cloud_rean2 + cloud_norm_7_rean2 +      rhum_rean2 + rhum_norm_7_rean2 + wind_rean2 + wind_norm_7_rean2 |      userID + unique_day_no + stateyrmn | 0 | state, data = Climate_Controls_DF,      na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "     Min       1Q   Median       3Q      Max \n",
       "-0.91323 -0.10806 -0.04915 -0.01406  1.10677 \n",
       "\n",
       "Coefficients:\n",
       "                     Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin                4.164e-04    6.137e-05   6.785 1.16e-11 ***\n",
       "tmin_norm_w7       -1.072e-05    2.283e-04  -0.047 0.962558    \n",
       "prcp               -1.650e-04    3.702e-04  -0.446 0.655764    \n",
       "prcp_norm_w7       -2.299e-03    3.448e-03  -0.667 0.504984    \n",
       "trange              2.098e-04    6.194e-05   3.386 0.000708 ***\n",
       "trange_norm_w7     -9.235e-05    3.345e-04  -0.276 0.782504    \n",
       "cloud_rean2        -1.140e-05    6.697e-06  -1.702 0.088685 .  \n",
       "cloud_norm_7_rean2  1.197e-04    6.433e-05   1.861 0.062731 .  \n",
       "rhum_rean2          3.256e-06    1.759e-05   0.185 0.853138    \n",
       "rhum_norm_7_rean2   5.957e-06    1.398e-04   0.043 0.966001    \n",
       "wind_rean2          9.007e-05    5.489e-05   1.641 0.100802    \n",
       "wind_norm_7_rean2   4.750e-04    4.877e-04   0.974 0.330099    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 0.2762 on 4345352 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.1145   Adjusted R-squared: 0.1023 \n",
       "Multiple R-squared(proj model): 1.58e-05   Adjusted R-squared: -0.01375 \n",
       "F-statistic(full model, *iid*):9.395 on 59818 and 4345352 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 5.516 on 12 and 1074 DF, p-value: 3.325e-09 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #shortsleep<5hr\n",
    "# linear_prob_5hr <- felm(formula = short_5hr_sleep ~ tmin + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(linear_prob_5hr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{S2. Nighttime minimum temperature and short sleep linear probability models} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lccc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{3}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-4} \n",
      " & Change in probability <7 hrs sleep & Change in probability <6 hrs sleep & Change in probability <5 hrs sleep \\\\ \n",
      "\\\\[-1.8ex] & (1) & (2) & (3)\\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " TMIN & 0.001$^{***}$ & 0.001$^{***}$ & 0.0004$^{***}$ \\\\ \n",
      "  & (0.0001) & (0.0001) & (0.0001) \\\\ \n",
      "  TMIN.NORM & 0.001$^{**}$ & 0.0002 & $-$0.00001 \\\\ \n",
      "  & (0.0005) & (0.0003) & (0.0002) \\\\ \n",
      "  PRCP & $-$0.002$^{**}$ & $-$0.001$^{***}$ & $-$0.0002 \\\\ \n",
      "  & (0.001) & (0.0004) & (0.0004) \\\\ \n",
      "  PRCP.NORM & $-$0.005 & 0.008 & $-$0.002 \\\\ \n",
      "  & (0.005) & (0.005) & (0.003) \\\\ \n",
      "  TRANGE & 0.001$^{***}$ & 0.0005$^{***}$ & 0.0002$^{***}$ \\\\ \n",
      "  & (0.0001) & (0.0001) & (0.0001) \\\\ \n",
      "  TRANGE.NORM & $-$0.001 & $-$0.0003 & $-$0.0001 \\\\ \n",
      "  & (0.001) & (0.001) & (0.0003) \\\\ \n",
      "  CLOUD & $-$0.00004$^{***}$ & $-$0.00002$^{**}$ & $-$0.00001$^{*}$ \\\\ \n",
      "  & (0.00001) & (0.00001) & (0.00001) \\\\ \n",
      "  CLOUD.NORM & 0.0002 & 0.0002$^{*}$ & 0.0001$^{*}$ \\\\ \n",
      "  & (0.0001) & (0.0001) & (0.0001) \\\\ \n",
      "  HUMID & 0.00002 & $-$0.00000 & 0.00000 \\\\ \n",
      "  & (0.00004) & (0.00003) & (0.00002) \\\\ \n",
      "  HUMID.NORM & $-$0.001$^{***}$ & $-$0.0004$^{**}$ & 0.00001 \\\\ \n",
      "  & (0.0002) & (0.0002) & (0.0001) \\\\ \n",
      "  WIND & $-$0.0005$^{***}$ & $-$0.00002 & 0.0001 \\\\ \n",
      "  & (0.0001) & (0.0001) & (0.0001) \\\\ \n",
      "  WIND.NORM & 0.002$^{*}$ & $-$0.0002 & 0.0005 \\\\ \n",
      "  & (0.001) & (0.001) & (0.0005) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes & Yes & Yes \\\\ \n",
      "Date FE & Yes & Yes & Yes \\\\ \n",
      "State:Month FE & Yes & Yes & Yes \\\\ \n",
      "Observations & 4,405,171 & 4,405,171 & 4,405,171 \\\\ \n",
      "R$^{2}$ & 0.236 & 0.201 & 0.115 \\\\ \n",
      "Adjusted R$^{2}$ & 0.225 & 0.190 & 0.102 \\\\ \n",
      "Residual Std. Error & 0.440 & 0.398 & 0.276 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{3}{r}{Standard errors are in parentheses and are clustered on the state admin level} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# invisible(stargazer(linear_prob_7hr, linear_prob_6hr, linear_prob_5hr,\n",
    "#           type='latex',\n",
    "#           title = \"Nighttime minimum temperature and change in short sleep duration probability (linear probability models)\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('<7 hrs sleep', '<6 hrs sleep', '<5 hrs sleep'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Short Sleep (1 = yes, 0 = no)',\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the state admin level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Nighttime Minimum Temperature, Sleep Onset, Midsleep and Offset Timing Linear FE Regressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = onset ~ tmin + tmin_norm_w7 + prcp + prcp_norm_w7 +      trange + trange_norm_w7 + cloud_rean2 + cloud_norm_7_rean2 +      rhum_rean2 + rhum_norm_7_rean2 + wind_rean2 + wind_norm_7_rean2 |      userID + unique_day_no + stateyrmn | 0 | state, data = Climate_Controls_DF,      na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "     Min       1Q   Median       3Q      Max \n",
       "-1698.90   -37.54     4.41    49.75   990.58 \n",
       "\n",
       "Coefficients:\n",
       "                    Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin                0.256826     0.033698   7.621 2.51e-14 ***\n",
       "tmin_norm_w7        0.124765     0.127682   0.977   0.3285    \n",
       "prcp                0.235901     0.175374   1.345   0.1786    \n",
       "prcp_norm_w7        3.731451     1.533232   2.434   0.0149 *  \n",
       "trange              0.045320     0.032162   1.409   0.1588    \n",
       "trange_norm_w7     -0.008036     0.247427  -0.032   0.9741    \n",
       "cloud_rean2         0.006234     0.003779   1.650   0.0990 .  \n",
       "cloud_norm_7_rean2  0.157804     0.040373   3.909 9.28e-05 ***\n",
       "rhum_rean2          0.014006     0.013346   1.049   0.2940    \n",
       "rhum_norm_7_rean2  -0.407806     0.078919  -5.167 2.37e-07 ***\n",
       "wind_rean2          0.146905     0.034132   4.304 1.68e-05 ***\n",
       "wind_norm_7_rean2  -0.248135     0.313097  -0.793   0.4281    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 137.1 on 4345352 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2363   Adjusted R-squared: 0.2258 \n",
       "Multiple R-squared(proj model): 6.149e-05   Adjusted R-squared: -0.0137 \n",
       "F-statistic(full model, *iid*):22.48 on 59818 and 4345352 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 13.94 on 12 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #ONSET\n",
    "# linear_t_onset <- felm(formula = onset ~ tmin + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(linear_t_onset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = midsleep ~ tmin + tmin_norm_w7 + prcp + prcp_norm_w7 +      trange + trange_norm_w7 + cloud_rean2 + cloud_norm_7_rean2 +      rhum_rean2 + rhum_norm_7_rean2 + wind_rean2 + wind_norm_7_rean2 |      userID + unique_day_no + stateyrmn | 0 | state, data = Climate_Controls_DF,      na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "     Min       1Q   Median       3Q      Max \n",
       "-1834.78   -34.54    -2.26    32.28   642.62 \n",
       "\n",
       "Coefficients:\n",
       "                    Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin                0.070643     0.024473   2.887 0.003894 ** \n",
       "tmin_norm_w7        0.164768     0.108459   1.519 0.128717    \n",
       "prcp                0.451939     0.120453   3.752 0.000175 ***\n",
       "prcp_norm_w7        5.059767     1.235462   4.095 4.21e-05 ***\n",
       "trange             -0.096567     0.019487  -4.955 7.22e-07 ***\n",
       "trange_norm_w7      0.035055     0.157945   0.222 0.824359    \n",
       "cloud_rean2         0.009343     0.002245   4.162 3.15e-05 ***\n",
       "cloud_norm_7_rean2  0.072550     0.029192   2.485 0.012944 *  \n",
       "rhum_rean2          0.013135     0.010963   1.198 0.230905    \n",
       "rhum_norm_7_rean2  -0.216588     0.058930  -3.675 0.000238 ***\n",
       "wind_rean2          0.241276     0.025474   9.471  < 2e-16 ***\n",
       "wind_norm_7_rean2  -0.253719     0.173459  -1.463 0.143550    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 67.08 on 4345352 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.5075   Adjusted R-squared: 0.5007 \n",
       "Multiple R-squared(proj model): 0.0002321   Adjusted R-squared: -0.01353 \n",
       "F-statistic(full model, *iid*):74.84 on 59818 and 4345352 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 39.52 on 12 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #MIDSLEEP\n",
    "# linear_t_midsleep <- felm(formula = midsleep ~ tmin + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(linear_t_midsleep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = offset ~ tmin + tmin_norm_w7 + prcp + prcp_norm_w7 +      trange + trange_norm_w7 + cloud_rean2 + cloud_norm_7_rean2 +      rhum_rean2 + rhum_norm_7_rean2 + wind_rean2 + wind_norm_7_rean2 |      userID + unique_day_no + stateyrmn | 0 | state, data = Climate_Controls_DF,      na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-668.52  -38.69   -2.80   36.05  618.27 \n",
       "\n",
       "Coefficients:\n",
       "                    Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin               -0.078367     0.032435  -2.416  0.01569 *  \n",
       "tmin_norm_w7        0.088654     0.126541   0.701  0.48355    \n",
       "prcp                0.687658     0.162226   4.239 2.25e-05 ***\n",
       "prcp_norm_w7        6.479047     1.313858   4.931 8.17e-07 ***\n",
       "trange             -0.203034     0.023380  -8.684  < 2e-16 ***\n",
       "trange_norm_w7      0.028677     0.183865   0.156  0.87606    \n",
       "cloud_rean2         0.014785     0.002826   5.231 1.69e-07 ***\n",
       "cloud_norm_7_rean2  0.044653     0.036621   1.219  0.22272    \n",
       "rhum_rean2          0.012632     0.013320   0.948  0.34295    \n",
       "rhum_norm_7_rean2  -0.155282     0.056750  -2.736  0.00621 ** \n",
       "wind_rean2          0.308837     0.029800  10.364  < 2e-16 ***\n",
       "wind_norm_7_rean2  -0.283006     0.192470  -1.470  0.14146    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 75.81 on 4345352 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.4822   Adjusted R-squared: 0.4751 \n",
       "Multiple R-squared(proj model): 0.0002836   Adjusted R-squared: -0.01348 \n",
       "F-statistic(full model, *iid*):67.66 on 59818 and 4345352 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 43.46 on 12 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #OFFSET\n",
    "# linear_t_offset <- felm(formula = offset ~ tmin + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(linear_t_offset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{S3. Nighttime minimum temperature and sleep onset, midsleep and offset timing} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lccc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{3}{c}{Dependent Variable: Sleep Timing (Minutes)} \\\\ \n",
      "\\cline{2-4} \n",
      " & Change in sleep onset time & Change in midsleep time & Change in sleep offset time \\\\ \n",
      "\\\\[-1.8ex] & (1) & (2) & (3)\\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " TMIN & 0.257$^{***}$ & 0.071$^{***}$ & $-$0.078$^{**}$ \\\\ \n",
      "  & (0.034) & (0.024) & (0.032) \\\\ \n",
      "  TMIN.NORM & 0.125 & 0.165 & 0.089 \\\\ \n",
      "  & (0.128) & (0.108) & (0.127) \\\\ \n",
      "  PRCP & 0.236 & 0.452$^{***}$ & 0.688$^{***}$ \\\\ \n",
      "  & (0.175) & (0.120) & (0.162) \\\\ \n",
      "  PRCP.NORM & 3.731$^{**}$ & 5.060$^{***}$ & 6.479$^{***}$ \\\\ \n",
      "  & (1.533) & (1.235) & (1.314) \\\\ \n",
      "  TRANGE & 0.045 & $-$0.097$^{***}$ & $-$0.203$^{***}$ \\\\ \n",
      "  & (0.032) & (0.019) & (0.023) \\\\ \n",
      "  TRANGE.NORM & $-$0.008 & 0.035 & 0.029 \\\\ \n",
      "  & (0.247) & (0.158) & (0.184) \\\\ \n",
      "  CLOUD & 0.006$^{*}$ & 0.009$^{***}$ & 0.015$^{***}$ \\\\ \n",
      "  & (0.004) & (0.002) & (0.003) \\\\ \n",
      "  CLOUD.NORM & 0.158$^{***}$ & 0.073$^{**}$ & 0.045 \\\\ \n",
      "  & (0.040) & (0.029) & (0.037) \\\\ \n",
      "  HUMID & 0.014 & 0.013 & 0.013 \\\\ \n",
      "  & (0.013) & (0.011) & (0.013) \\\\ \n",
      "  HUMID.NORM & $-$0.408$^{***}$ & $-$0.217$^{***}$ & $-$0.155$^{***}$ \\\\ \n",
      "  & (0.079) & (0.059) & (0.057) \\\\ \n",
      "  WIND & 0.147$^{***}$ & 0.241$^{***}$ & 0.309$^{***}$ \\\\ \n",
      "  & (0.034) & (0.025) & (0.030) \\\\ \n",
      "  WIND.NORM & $-$0.248 & $-$0.254 & $-$0.283 \\\\ \n",
      "  & (0.313) & (0.173) & (0.192) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes & Yes & Yes \\\\ \n",
      "Date FE & Yes & Yes & Yes \\\\ \n",
      "State:Month FE & Yes & Yes & Yes \\\\ \n",
      "Observations & 4,405,171 & 4,405,171 & 4,405,171 \\\\ \n",
      "R$^{2}$ & 0.236 & 0.507 & 0.482 \\\\ \n",
      "Adjusted R$^{2}$ & 0.226 & 0.501 & 0.475 \\\\ \n",
      "Residual Std. Error & 137.063 & 67.082 & 75.811 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{3}{r}{Standard errors are in parentheses and are clustered on the state admin level} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# invisible(stargazer(linear_t_onset, linear_t_midsleep, linear_t_offset,\n",
    "#           type='latex',\n",
    "#           title = \"Nighttime minimum temperature and sleep onset, midsleep and offset timing\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Change in sleep onset time', 'Change in midsleep time', 'Change in sleep offset time'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Timing (Minutes)',\n",
    "#           covariate.labels = c(\"TMIN\", \"TMIN.NORM\", \"PRCP\", \"PRCP.NORM\", \"TRANGE\", \"TRANGE.NORM\", \"CLOUD\", \"CLOUD.NORM\", \"HUMID\", \"HUMID.NORM\", \"WIND\", \"WIND.NORM\"),\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the state admin level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Linear Nighttime Heat Index and Sleep Duration FE Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ heat.index + tmin_norm_w7 +      prcp + prcp_norm_w7 + trange + trange_norm_w7 + cloud_rean2 +      cloud_norm_7_rean2 + +rhum_norm_7_rean2 + wind_rean2 + wind_norm_7_rean2 |      userID + unique_day_no + stateyrmn | 0 | state, data = Climate_Controls_DF,      na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-384.49  -49.12   -2.32   45.76  407.70 \n",
       "\n",
       "Coefficients:\n",
       "                    Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "heat.index         -0.265320     0.023717 -11.187  < 2e-16 ***\n",
       "tmin_norm_w7       -0.179906     0.085358  -2.108  0.03506 *  \n",
       "prcp                0.500146     0.112426   4.449 8.64e-06 ***\n",
       "prcp_norm_w7        2.080463     1.094737   1.900  0.05738 .  \n",
       "trange             -0.215653     0.022310  -9.666  < 2e-16 ***\n",
       "trange_norm_w7      0.060295     0.148125   0.407  0.68397    \n",
       "cloud_rean2         0.010778     0.002274   4.740 2.14e-06 ***\n",
       "cloud_norm_7_rean2 -0.038931     0.023952  -1.625  0.10409    \n",
       "rhum_norm_7_rean2   0.109534     0.039684   2.760  0.00578 ** \n",
       "wind_rean2          0.125459     0.022279   5.631 1.79e-08 ***\n",
       "wind_norm_7_rean2  -0.293056     0.188222  -1.557  0.11948    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.71 on 4345353 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2842   Adjusted R-squared: 0.2744 \n",
       "Multiple R-squared(proj model): 0.0001521   Adjusted R-squared: -0.01361 \n",
       "F-statistic(full model, *iid*):28.84 on 59817 and 4345353 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 45.11 on 11 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #linear_hi <- felm(formula = sleep_duration_minutes ~ heat.index + tmin_norm_w7 + prcp + prcp_norm_w7 + trange + trange_norm_w7 + cloud_rean2 + cloud_norm_7_rean2 + + rhum_norm_7_rean2 + wind_rean2 + wind_norm_7_rean2 | userID + unique_day_no + stateyrmn | 0 | state, data = Climate_Controls_DF, na.action = na.omit)\n",
    "# linear_hi <- felm(formula = sleep_duration_minutes ~ heat.index + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(linear_hi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{S5. Nighttime TMIN Heat Index regression} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{1}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-2} \n",
      " & Linear HEAT.INDEX \\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " HEAT.INDEX & $-$0.265$^{***}$ \\\\ \n",
      "  & (0.024) \\\\ \n",
      "  TMIN.NORM & $-$0.180$^{**}$ \\\\ \n",
      "  & (0.085) \\\\ \n",
      "  PRCP & 0.500$^{***}$ \\\\ \n",
      "  & (0.112) \\\\ \n",
      "  PRCP.NORM & 2.080$^{*}$ \\\\ \n",
      "  & (1.095) \\\\ \n",
      "  TRANGE & $-$0.216$^{***}$ \\\\ \n",
      "  & (0.022) \\\\ \n",
      "  TRANGE.NORM & 0.060 \\\\ \n",
      "  & (0.148) \\\\ \n",
      "  CLOUD & 0.011$^{***}$ \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  CLOUD.NORM & $-$0.039 \\\\ \n",
      "  & (0.024) \\\\ \n",
      "  HUMID.NORM & 0.110$^{***}$ \\\\ \n",
      "  & (0.040) \\\\ \n",
      "  WIND & 0.125$^{***}$ \\\\ \n",
      "  & (0.022) \\\\ \n",
      "  WIND.NORM & $-$0.293 \\\\ \n",
      "  & (0.188) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes \\\\ \n",
      "Date FE & Yes \\\\ \n",
      "State:Month FE & Yes \\\\ \n",
      "Observations & 4,405,171 \\\\ \n",
      "R$^{2}$ & 0.284 \\\\ \n",
      "Adjusted R$^{2}$ & 0.274 \\\\ \n",
      "Residual Std. Error & 77.708 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{1}{r}{Standard errors are in parentheses and are clustered on the state admin level.} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# invisible(stargazer(linear_hi,\n",
    "#           type='latex',\n",
    "#           title = \"S5. Nighttime TMIN Heat Index regression\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Linear HEAT.INDEX'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           covariate.labels = c(\"HEAT.INDEX\", \"TMIN.NORM\", \"PRCP\", \"PRCP.NORM\", \"TRANGE\", \"TRANGE.NORM\", \"CLOUD\", \"CLOUD.NORM\", \"HUMID.NORM\", \"WIND\", \"WIND.NORM\"),\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the state admin level.'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Alternative Regression Specification: Nighttime Minimum Temperature Anomalies and Sleep Duration FE Regressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tanomaly_norm_w7 + prcp +      prcp_norm_w7 + trange + trange_norm_w7 + cloud_rean2 + cloud_norm_7_rean2 +      rhum_rean2 + rhum_norm_7_rean2 + wind_rean2 + wind_norm_7_rean2 |      userID + unique_day_no + stateyrmn | 0 | state, data = Climate_Controls_DF,      na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-384.71  -49.12   -2.32   45.76  407.73 \n",
       "\n",
       "Coefficients:\n",
       "                     Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tanomaly_norm_w7   -0.2827072    0.0260611 -10.848  < 2e-16 ***\n",
       "prcp                0.4848159    0.1202228   4.033 5.52e-05 ***\n",
       "prcp_norm_w7        2.4057613    1.1228333   2.143  0.03215 *  \n",
       "trange             -0.2052606    0.0227148  -9.036  < 2e-16 ***\n",
       "trange_norm_w7      0.2642430    0.1422570   1.858  0.06324 .  \n",
       "cloud_rean2         0.0100539    0.0023694   4.243 2.20e-05 ***\n",
       "cloud_norm_7_rean2 -0.0578531    0.0241895  -2.392  0.01677 *  \n",
       "rhum_rean2          0.0008509    0.0074829   0.114  0.90946    \n",
       "rhum_norm_7_rean2   0.1315660    0.0454880   2.892  0.00382 ** \n",
       "wind_rean2          0.1284060    0.0218202   5.885 3.99e-09 ***\n",
       "wind_norm_7_rean2  -0.2333418    0.2065479  -1.130  0.25859    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.71 on 4345353 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2842   Adjusted R-squared: 0.2743 \n",
       "Multiple R-squared(proj model): 0.0001326   Adjusted R-squared: -0.01363 \n",
       "F-statistic(full model, *iid*):28.84 on 59817 and 4345353 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 44.24 on 11 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# linear_tanomaly <- felm(formula = sleep_duration_minutes ~ tanomaly_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state,\n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(linear_tanomaly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tanomaly_norm_7_rean2 +      prcp_rean2 + prcp_norm_7_rean2 + trange_rean2 + trange_norm_7_rean2 +      cloud_rean2 + cloud_norm_7_rean2 + rhum_rean2 + rhum_norm_7_rean2 +      wind_rean2 + wind_norm_7_rean2 | userID + unique_day_no +      stateyrmn | 0 | state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-383.99  -49.17   -2.04   46.20  403.03 \n",
       "\n",
       "Coefficients:\n",
       "                       Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tanomaly_norm_7_rean2 -0.280692     0.019865 -14.130  < 2e-16 ***\n",
       "prcp_rean2             0.295572     0.073493   4.022 5.78e-05 ***\n",
       "prcp_norm_7_rean2     -0.655787     0.867804  -0.756 0.449837    \n",
       "trange_rean2          -0.235382     0.022954 -10.255  < 2e-16 ***\n",
       "trange_norm_7_rean2    0.113951     0.092207   1.236 0.216530    \n",
       "cloud_rean2            0.015567     0.001856   8.386  < 2e-16 ***\n",
       "cloud_norm_7_rean2    -0.046794     0.026213  -1.785 0.074243 .  \n",
       "rhum_rean2             0.008178     0.005368   1.523 0.127644    \n",
       "rhum_norm_7_rean2      0.125979     0.036631   3.439 0.000584 ***\n",
       "wind_rean2             0.097674     0.016293   5.995 2.04e-09 ***\n",
       "wind_norm_7_rean2     -0.187913     0.170190  -1.104 0.269532    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.94 on 7105706 degrees of freedom\n",
       "  (236359 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2758   Adjusted R-squared: 0.2687 \n",
       "Multiple R-squared(proj model): 0.0001345   Adjusted R-squared: -0.009561 \n",
       "F-statistic(full model, *iid*):39.27 on 68905 and 7105706 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 59.74 on 11 and 1256 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# linear_tanomaly_rean2 <- felm(formula = sleep_duration_minutes ~ tanomaly_norm_7_rean2 + \n",
    "#                                           prcp_rean2 + prcp_norm_7_rean2 +\n",
    "#                                           trange_rean2 + trange_norm_7_rean2 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state,\n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(linear_tanomaly_rean2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_24hr_minutes ~ tanomaly_norm_w7 +      prcp + prcp_norm_w7 + trange + trange_norm_w7 + cloud_rean2 +      cloud_norm_7_rean2 + rhum_rean2 + rhum_norm_7_rean2 + wind_rean2 +      wind_norm_7_rean2 | userID + unique_day_no + stateyrmn |      0 | state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-406.37  -52.19   -3.61   47.40  537.02 \n",
       "\n",
       "Coefficients:\n",
       "                     Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tanomaly_norm_w7   -0.3088993    0.0278091 -11.108  < 2e-16 ***\n",
       "prcp                0.4637438    0.1335617   3.472 0.000516 ***\n",
       "prcp_norm_w7        2.1913684    1.2082819   1.814 0.069736 .  \n",
       "trange             -0.2415393    0.0246160  -9.812  < 2e-16 ***\n",
       "trange_norm_w7      0.3193891    0.1546707   2.065 0.038927 *  \n",
       "cloud_rean2         0.0110194    0.0028175   3.911 9.19e-05 ***\n",
       "cloud_norm_7_rean2 -0.0685106    0.0260666  -2.628 0.008582 ** \n",
       "rhum_rean2          0.0006934    0.0079309   0.087 0.930333    \n",
       "rhum_norm_7_rean2   0.1182056    0.0428737   2.757 0.005832 ** \n",
       "wind_rean2          0.1510691    0.0258171   5.852 4.87e-09 ***\n",
       "wind_norm_7_rean2  -0.2117332    0.2327473  -0.910 0.362974    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 82.5 on 4345353 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2746   Adjusted R-squared: 0.2646 \n",
       "Multiple R-squared(proj model): 0.0001419   Adjusted R-squared: -0.01362 \n",
       "F-statistic(full model, *iid*):27.49 on 59817 and 4345353 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 45.44 on 11 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# linear_tanomaly_24hr <- felm(formula = sleep_duration_24hr_minutes ~ tanomaly_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state,\n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(linear_tanomaly_24hr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{S6. Main Effects: Nighttime minimum temperature anomalies and sleep duration regressions} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lccc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{3}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-4} \n",
      " & Linear TMIN.Anomaly GHCND & Linear TMIN.Anomaly Reanalysis 2 & Linear TMIN.Anomaly GHCND 24hr sleep \\\\ \n",
      "\\\\[-1.8ex] & (1) & (2) & (3)\\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " TMIN.ANOMALY & $-$0.283$^{***}$ &  & $-$0.309$^{***}$ \\\\ \n",
      "  & (0.026) &  & (0.028) \\\\ \n",
      "  PRCP & 0.485$^{***}$ &  & 0.464$^{***}$ \\\\ \n",
      "  & (0.120) &  & (0.134) \\\\ \n",
      "  PRCP.NORM & 2.406$^{**}$ &  & 2.191$^{*}$ \\\\ \n",
      "  & (1.123) &  & (1.208) \\\\ \n",
      "  TRANGE & $-$0.205$^{***}$ &  & $-$0.242$^{***}$ \\\\ \n",
      "  & (0.023) &  & (0.025) \\\\ \n",
      "  TRANGE.NORM & 0.264$^{*}$ &  & 0.319$^{**}$ \\\\ \n",
      "  & (0.142) &  & (0.155) \\\\ \n",
      "  CLOUD &  & $-$0.281$^{***}$ &  \\\\ \n",
      "  &  & (0.020) &  \\\\ \n",
      "  CLOUD.NORM &  & 0.296$^{***}$ &  \\\\ \n",
      "  &  & (0.073) &  \\\\ \n",
      "  HUMID &  & $-$0.656 &  \\\\ \n",
      "  &  & (0.868) &  \\\\ \n",
      "  HUMID.NORM &  & $-$0.235$^{***}$ &  \\\\ \n",
      "  &  & (0.023) &  \\\\ \n",
      "  WIND &  & 0.114 &  \\\\ \n",
      "  &  & (0.092) &  \\\\ \n",
      "  WIND.NORM & 0.010$^{***}$ & 0.016$^{***}$ & 0.011$^{***}$ \\\\ \n",
      "  & (0.002) & (0.002) & (0.003) \\\\ \n",
      "  cloud\\_norm\\_7\\_rean2 & $-$0.058$^{**}$ & $-$0.047$^{*}$ & $-$0.069$^{***}$ \\\\ \n",
      "  & (0.024) & (0.026) & (0.026) \\\\ \n",
      "  rhum\\_rean2 & 0.001 & 0.008 & 0.001 \\\\ \n",
      "  & (0.007) & (0.005) & (0.008) \\\\ \n",
      "  rhum\\_norm\\_7\\_rean2 & 0.132$^{***}$ & 0.126$^{***}$ & 0.118$^{***}$ \\\\ \n",
      "  & (0.045) & (0.037) & (0.043) \\\\ \n",
      "  wind\\_rean2 & 0.128$^{***}$ & 0.098$^{***}$ & 0.151$^{***}$ \\\\ \n",
      "  & (0.022) & (0.016) & (0.026) \\\\ \n",
      "  wind\\_norm\\_7\\_rean2 & $-$0.233 & $-$0.188 & $-$0.212 \\\\ \n",
      "  & (0.207) & (0.170) & (0.233) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes & Yes & Yes \\\\ \n",
      "Date FE & Yes & Yes & Yes \\\\ \n",
      "State:Month FE & Yes & Yes & Yes \\\\ \n",
      "Observations & 4,405,171 & 7,174,612 & 4,405,171 \\\\ \n",
      "R$^{2}$ & 0.284 & 0.276 & 0.275 \\\\ \n",
      "Adjusted R$^{2}$ & 0.274 & 0.269 & 0.265 \\\\ \n",
      "Residual Std. Error & 77.709 & 77.939 & 82.497 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{3}{r}{Standard errors are in parentheses and are clustered on the state admin level} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# invisible(stargazer(linear_tanomaly, linear_tanomaly_rean2, linear_tanomaly_24hr,\n",
    "#           type='latex',\n",
    "#           title = \"Nighttime minimum temperature anomalies and sleep duration regressions\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Linear TMIN.Anomaly GHCND', 'Linear TMIN.Anomaly Reanalysis 2', 'Linear TMIN.Anomaly GHCND 24hr sleep'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           covariate.labels = c(\"TMIN.ANOMALY\", \"PRCP\", \"PRCP.NORM\", \"TRANGE\", \"TRANGE.NORM\", \"CLOUD\", \"CLOUD.NORM\", \"HUMID\", \"HUMID.NORM\", \"WIND\", \"WIND.NORM\"),\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the state admin level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{S6B. Main Effects: Nighttime minimum temperature anomalies and sleep duration regressions} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{1}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-2} \n",
      " & Linear TMIN.Anomaly Reanalysis 2 \\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " TMIN.ANOMALY & $-$0.281$^{***}$ \\\\ \n",
      "  & (0.020) \\\\ \n",
      "  PRCP & 0.296$^{***}$ \\\\ \n",
      "  & (0.073) \\\\ \n",
      "  PRCP.NORM & $-$0.656 \\\\ \n",
      "  & (0.868) \\\\ \n",
      "  TRANGE & $-$0.235$^{***}$ \\\\ \n",
      "  & (0.023) \\\\ \n",
      "  TRANGE.NORM & 0.114 \\\\ \n",
      "  & (0.092) \\\\ \n",
      "  CLOUD & 0.016$^{***}$ \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  CLOUD.NORM & $-$0.047$^{*}$ \\\\ \n",
      "  & (0.026) \\\\ \n",
      "  HUMID & 0.008 \\\\ \n",
      "  & (0.005) \\\\ \n",
      "  HUMID.NORM & 0.126$^{***}$ \\\\ \n",
      "  & (0.037) \\\\ \n",
      "  WIND & 0.098$^{***}$ \\\\ \n",
      "  & (0.016) \\\\ \n",
      "  WIND.NORM & $-$0.188 \\\\ \n",
      "  & (0.170) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes \\\\ \n",
      "Date FE & Yes \\\\ \n",
      "State:Month FE & Yes \\\\ \n",
      "Observations & 7,174,612 \\\\ \n",
      "R$^{2}$ & 0.276 \\\\ \n",
      "Adjusted R$^{2}$ & 0.269 \\\\ \n",
      "Residual Std. Error & 77.939 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{1}{r}{Standard errors are in parentheses and are clustered on the state admin level} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# invisible(stargazer(linear_tanomaly_rean2,\n",
    "#           type='latex',\n",
    "#           title = \"Nighttime minimum temperature anomalies and sleep duration regressions\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Linear TMIN.Anomaly Reanalysis 2'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           covariate.labels = c(\"TMIN.ANOMALY\", \"PRCP\", \"PRCP.NORM\", \"TRANGE\", \"TRANGE.NORM\", \"CLOUD\", \"CLOUD.NORM\", \"HUMID\", \"HUMID.NORM\", \"WIND\", \"WIND.NORM\"),\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the state admin level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Subgroup Analysis: Nighttime Minimum Temperature and Sleep Duration, Age Interaction Model (Alternative 10yr. Age Group Intervals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmin + tmin:age_group +      tmin_norm_w7 + prcp + prcp_norm_w7 + trange + trange_norm_w7 +      cloud_rean2 + cloud_norm_7_rean2 + rhum_rean2 + rhum_norm_7_rean2 +      wind_rean2 + wind_norm_7_rean2 | userID + unique_day_no +      stateyrmn | 0 | state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-384.31  -49.12   -2.32   45.76  407.51 \n",
       "\n",
       "Coefficients:\n",
       "                          Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin                    -0.3127692    0.0319539  -9.788  < 2e-16 ***\n",
       "tmin_norm_w7            -0.1778038    0.0851758  -2.087  0.03684 *  \n",
       "prcp                     0.4840307    0.1203805   4.021 5.80e-05 ***\n",
       "prcp_norm_w7             2.1436889    1.0855485   1.975  0.04830 *  \n",
       "trange                  -0.2077027    0.0226284  -9.179  < 2e-16 ***\n",
       "trange_norm_w7           0.0585543    0.1470175   0.398  0.69042    \n",
       "cloud_rean2              0.0101385    0.0023772   4.265 2.00e-05 ***\n",
       "cloud_norm_7_rean2      -0.0391626    0.0237360  -1.650  0.09896 .  \n",
       "rhum_rean2               0.0004981    0.0075200   0.066  0.94719    \n",
       "rhum_norm_7_rean2        0.1040923    0.0398132   2.615  0.00894 ** \n",
       "wind_rean2               0.1278223    0.0218597   5.847 4.99e-09 ***\n",
       "wind_norm_7_rean2       -0.2921906    0.1867375  -1.565  0.11765    \n",
       "tmin:age_group(30,40]    0.0377142    0.0253889   1.485  0.13742    \n",
       "tmin:age_group(40,50]    0.0559214    0.0251451   2.224  0.02615 *  \n",
       "tmin:age_group(50,60]    0.0230785    0.0281090   0.821  0.41163    \n",
       "tmin:age_group(60,70]   -0.2276274    0.0443618  -5.131 2.88e-07 ***\n",
       "tmin:age_group(70, Inf] -0.2657566    0.1048258  -2.535  0.01124 *  \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.71 on 4345347 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2842   Adjusted R-squared: 0.2744 \n",
       "Multiple R-squared(proj model): 0.000193   Adjusted R-squared: -0.01357 \n",
       "F-statistic(full model, *iid*):28.84 on 59823 and 4345347 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 38.22 on 17 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #10 yr. age robustness check\n",
    "# setDT(Climate_Controls_DF)\n",
    "# #10yr age groups\n",
    "# levels(Climate_Controls_DF$age_group)\n",
    "\n",
    "# linear_t_age_10 <- felm(formula = sleep_duration_minutes ~ tmin + tmin:age_group + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "# summary(linear_t_age_10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{Marginal Effect of Tmin: Alternative 10yr. Age Groups} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{1}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-2} \n",
      " & Base Level: <30years \\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " tmin & $-$0.313$^{***}$ \\\\ \n",
      "  & (0.032) \\\\ \n",
      "  tmin\\_norm\\_w7 & $-$0.178$^{**}$ \\\\ \n",
      "  & (0.085) \\\\ \n",
      "  prcp & 0.484$^{***}$ \\\\ \n",
      "  & (0.120) \\\\ \n",
      "  prcp\\_norm\\_w7 & 2.144$^{**}$ \\\\ \n",
      "  & (1.086) \\\\ \n",
      "  trange & $-$0.208$^{***}$ \\\\ \n",
      "  & (0.023) \\\\ \n",
      "  trange\\_norm\\_w7 & 0.059 \\\\ \n",
      "  & (0.147) \\\\ \n",
      "  cloud\\_rean2 & 0.010$^{***}$ \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  cloud\\_norm\\_7\\_rean2 & $-$0.039$^{*}$ \\\\ \n",
      "  & (0.024) \\\\ \n",
      "  rhum\\_rean2 & 0.0005 \\\\ \n",
      "  & (0.008) \\\\ \n",
      "  rhum\\_norm\\_7\\_rean2 & 0.104$^{***}$ \\\\ \n",
      "  & (0.040) \\\\ \n",
      "  wind\\_rean2 & 0.128$^{***}$ \\\\ \n",
      "  & (0.022) \\\\ \n",
      "  wind\\_norm\\_7\\_rean2 & $-$0.292 \\\\ \n",
      "  & (0.187) \\\\ \n",
      "  tmin:age\\_group(30,40] & 0.038 \\\\ \n",
      "  & (0.025) \\\\ \n",
      "  tmin:age\\_group(40,50] & 0.056$^{**}$ \\\\ \n",
      "  & (0.025) \\\\ \n",
      "  tmin:age\\_group(50,60] & 0.023 \\\\ \n",
      "  & (0.028) \\\\ \n",
      "  tmin:age\\_group(60,70] & $-$0.228$^{***}$ \\\\ \n",
      "  & (0.044) \\\\ \n",
      "  tmin:age\\_group(70, Inf] & $-$0.266$^{**}$ \\\\ \n",
      "  & (0.105) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes \\\\ \n",
      "Date FE & Yes \\\\ \n",
      "State:Month FE & Yes \\\\ \n",
      "Observations & 4,405,171 \\\\ \n",
      "R$^{2}$ & 0.284 \\\\ \n",
      "Adjusted R$^{2}$ & 0.274 \\\\ \n",
      "Residual Std. Error & 77.706 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{1}{r}{Standard errors are in parentheses and are clustered on the first admin level.} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# #Latex output\n",
    "# invisible(stargazer(linear_t_age_10,\n",
    "#           type='latex',\n",
    "#           title = \"Marginal Effect of Tmin: Alternative 10yr. Age Groups\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Base Level: <30years'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the first admin level.'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Subgroup Analysis: Nighttime Minimum Temperature and Sleep,Income (World Bank Country GNI Category) Interaction Model, Alternative Reference Category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmin + tmin:income_cat +      tmin_norm_w7 + prcp + prcp_norm_w7 + trange + trange_norm_w7 +      cloud_rean2 + cloud_norm_7_rean2 + rhum_rean2 + rhum_norm_7_rean2 +      wind_rean2 + wind_norm_7_rean2 | userID + unique_day_no +      stateyrmn | 0 | state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-384.38  -49.12   -2.32   45.76  407.70 \n",
       "\n",
       "Coefficients:\n",
       "                                     Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin                               -0.2340047    0.0484585  -4.829 1.37e-06 ***\n",
       "tmin_norm_w7                       -0.1757590    0.0847832  -2.073  0.03817 *  \n",
       "prcp                                0.4820972    0.1205263   4.000 6.34e-05 ***\n",
       "prcp_norm_w7                        2.1238418    1.0856055   1.956  0.05042 .  \n",
       "trange                             -0.2074072    0.0226457  -9.159  < 2e-16 ***\n",
       "trange_norm_w7                      0.0548613    0.1469892   0.373  0.70897    \n",
       "cloud_rean2                         0.0101664    0.0023857   4.261 2.03e-05 ***\n",
       "cloud_norm_7_rean2                 -0.0389902    0.0239339  -1.629  0.10330    \n",
       "rhum_rean2                          0.0007256    0.0075673   0.096  0.92361    \n",
       "rhum_norm_7_rean2                   0.1043327    0.0394745   2.643  0.00822 ** \n",
       "wind_rean2                          0.1275374    0.0218858   5.827 5.63e-09 ***\n",
       "wind_norm_7_rean2                  -0.2954294    0.1875176  -1.575  0.11515    \n",
       "tmin:income_catHigh income         -0.0684581    0.0522544  -1.310  0.19016    \n",
       "tmin:income_catLower middle income -0.6131605    0.3218282  -1.905  0.05675 .  \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.71 on 4345350 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2842   Adjusted R-squared: 0.2744 \n",
       "Multiple R-squared(proj model): 0.0001542   Adjusted R-squared: -0.01361 \n",
       "F-statistic(full model, *iid*):28.84 on 59820 and 4345350 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 37.09 on 14 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Estimate</th><th scope=col>Cluster s.e.</th><th scope=col>t value</th><th scope=col>Pr(&gt;|t|)</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>tmin</th><td>-0.2340047233</td><td>0.048458507  </td><td>-4.82897096  </td><td>1.372451e-06 </td></tr>\n",
       "\t<tr><th scope=row>tmin_norm_w7</th><td>-0.1757590106</td><td>0.084783196  </td><td>-2.07304064  </td><td>3.816856e-02 </td></tr>\n",
       "\t<tr><th scope=row>prcp</th><td> 0.4820972138</td><td>0.120526306  </td><td> 3.99993354  </td><td>6.336132e-05 </td></tr>\n",
       "\t<tr><th scope=row>prcp_norm_w7</th><td> 2.1238418038</td><td>1.085605506  </td><td> 1.95636609  </td><td>5.042211e-02 </td></tr>\n",
       "\t<tr><th scope=row>trange</th><td>-0.2074072336</td><td>0.022645696  </td><td>-9.15879263  </td><td>5.250294e-20 </td></tr>\n",
       "\t<tr><th scope=row>trange_norm_w7</th><td> 0.0548613466</td><td>0.146989196  </td><td> 0.37323387  </td><td>7.089744e-01 </td></tr>\n",
       "\t<tr><th scope=row>cloud_rean2</th><td> 0.0101664263</td><td>0.002385745  </td><td> 4.26132182  </td><td>2.032256e-05 </td></tr>\n",
       "\t<tr><th scope=row>cloud_norm_7_rean2</th><td>-0.0389901809</td><td>0.023933857  </td><td>-1.62908055  </td><td>1.032960e-01 </td></tr>\n",
       "\t<tr><th scope=row>rhum_rean2</th><td> 0.0007256275</td><td>0.007567287  </td><td> 0.09589006  </td><td>9.236079e-01 </td></tr>\n",
       "\t<tr><th scope=row>rhum_norm_7_rean2</th><td> 0.1043326523</td><td>0.039474499  </td><td> 2.64303929  </td><td>8.216579e-03 </td></tr>\n",
       "\t<tr><th scope=row>wind_rean2</th><td> 0.1275374373</td><td>0.021885812  </td><td> 5.82740252  </td><td>5.630067e-09 </td></tr>\n",
       "\t<tr><th scope=row>wind_norm_7_rean2</th><td>-0.2954294192</td><td>0.187517575  </td><td>-1.57547589  </td><td>1.151467e-01 </td></tr>\n",
       "\t<tr><th scope=row>tmin:income_catHigh income</th><td>-0.0684581201</td><td>0.052254374  </td><td>-1.31009358  </td><td>1.901642e-01 </td></tr>\n",
       "\t<tr><th scope=row>tmin:income_catLower middle income</th><td>-0.6131604588</td><td>0.321828156  </td><td>-1.90524181  </td><td>5.674871e-02 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & Estimate & Cluster s.e. & t value & Pr(>\\textbar{}t\\textbar{})\\\\\n",
       "\\hline\n",
       "\ttmin & -0.2340047233 & 0.048458507   & -4.82897096   & 1.372451e-06 \\\\\n",
       "\ttmin\\_norm\\_w7 & -0.1757590106 & 0.084783196   & -2.07304064   & 3.816856e-02 \\\\\n",
       "\tprcp &  0.4820972138 & 0.120526306   &  3.99993354   & 6.336132e-05 \\\\\n",
       "\tprcp\\_norm\\_w7 &  2.1238418038 & 1.085605506   &  1.95636609   & 5.042211e-02 \\\\\n",
       "\ttrange & -0.2074072336 & 0.022645696   & -9.15879263   & 5.250294e-20 \\\\\n",
       "\ttrange\\_norm\\_w7 &  0.0548613466 & 0.146989196   &  0.37323387   & 7.089744e-01 \\\\\n",
       "\tcloud\\_rean2 &  0.0101664263 & 0.002385745   &  4.26132182   & 2.032256e-05 \\\\\n",
       "\tcloud\\_norm\\_7\\_rean2 & -0.0389901809 & 0.023933857   & -1.62908055   & 1.032960e-01 \\\\\n",
       "\trhum\\_rean2 &  0.0007256275 & 0.007567287   &  0.09589006   & 9.236079e-01 \\\\\n",
       "\trhum\\_norm\\_7\\_rean2 &  0.1043326523 & 0.039474499   &  2.64303929   & 8.216579e-03 \\\\\n",
       "\twind\\_rean2 &  0.1275374373 & 0.021885812   &  5.82740252   & 5.630067e-09 \\\\\n",
       "\twind\\_norm\\_7\\_rean2 & -0.2954294192 & 0.187517575   & -1.57547589   & 1.151467e-01 \\\\\n",
       "\ttmin:income\\_catHigh income & -0.0684581201 & 0.052254374   & -1.31009358   & 1.901642e-01 \\\\\n",
       "\ttmin:income\\_catLower middle income & -0.6131604588 & 0.321828156   & -1.90524181   & 5.674871e-02 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | Estimate | Cluster s.e. | t value | Pr(>|t|) |\n",
       "|---|---|---|---|---|\n",
       "| tmin | -0.2340047233 | 0.048458507   | -4.82897096   | 1.372451e-06  |\n",
       "| tmin_norm_w7 | -0.1757590106 | 0.084783196   | -2.07304064   | 3.816856e-02  |\n",
       "| prcp |  0.4820972138 | 0.120526306   |  3.99993354   | 6.336132e-05  |\n",
       "| prcp_norm_w7 |  2.1238418038 | 1.085605506   |  1.95636609   | 5.042211e-02  |\n",
       "| trange | -0.2074072336 | 0.022645696   | -9.15879263   | 5.250294e-20  |\n",
       "| trange_norm_w7 |  0.0548613466 | 0.146989196   |  0.37323387   | 7.089744e-01  |\n",
       "| cloud_rean2 |  0.0101664263 | 0.002385745   |  4.26132182   | 2.032256e-05  |\n",
       "| cloud_norm_7_rean2 | -0.0389901809 | 0.023933857   | -1.62908055   | 1.032960e-01  |\n",
       "| rhum_rean2 |  0.0007256275 | 0.007567287   |  0.09589006   | 9.236079e-01  |\n",
       "| rhum_norm_7_rean2 |  0.1043326523 | 0.039474499   |  2.64303929   | 8.216579e-03  |\n",
       "| wind_rean2 |  0.1275374373 | 0.021885812   |  5.82740252   | 5.630067e-09  |\n",
       "| wind_norm_7_rean2 | -0.2954294192 | 0.187517575   | -1.57547589   | 1.151467e-01  |\n",
       "| tmin:income_catHigh income | -0.0684581201 | 0.052254374   | -1.31009358   | 1.901642e-01  |\n",
       "| tmin:income_catLower middle income | -0.6131604588 | 0.321828156   | -1.90524181   | 5.674871e-02  |\n",
       "\n"
      ],
      "text/plain": [
       "                                   Estimate      Cluster s.e. t value    \n",
       "tmin                               -0.2340047233 0.048458507  -4.82897096\n",
       "tmin_norm_w7                       -0.1757590106 0.084783196  -2.07304064\n",
       "prcp                                0.4820972138 0.120526306   3.99993354\n",
       "prcp_norm_w7                        2.1238418038 1.085605506   1.95636609\n",
       "trange                             -0.2074072336 0.022645696  -9.15879263\n",
       "trange_norm_w7                      0.0548613466 0.146989196   0.37323387\n",
       "cloud_rean2                         0.0101664263 0.002385745   4.26132182\n",
       "cloud_norm_7_rean2                 -0.0389901809 0.023933857  -1.62908055\n",
       "rhum_rean2                          0.0007256275 0.007567287   0.09589006\n",
       "rhum_norm_7_rean2                   0.1043326523 0.039474499   2.64303929\n",
       "wind_rean2                          0.1275374373 0.021885812   5.82740252\n",
       "wind_norm_7_rean2                  -0.2954294192 0.187517575  -1.57547589\n",
       "tmin:income_catHigh income         -0.0684581201 0.052254374  -1.31009358\n",
       "tmin:income_catLower middle income -0.6131604588 0.321828156  -1.90524181\n",
       "                                   Pr(>|t|)    \n",
       "tmin                               1.372451e-06\n",
       "tmin_norm_w7                       3.816856e-02\n",
       "prcp                               6.336132e-05\n",
       "prcp_norm_w7                       5.042211e-02\n",
       "trange                             5.250294e-20\n",
       "trange_norm_w7                     7.089744e-01\n",
       "cloud_rean2                        2.032256e-05\n",
       "cloud_norm_7_rean2                 1.032960e-01\n",
       "rhum_rean2                         9.236079e-01\n",
       "rhum_norm_7_rean2                  8.216579e-03\n",
       "wind_rean2                         5.630067e-09\n",
       "wind_norm_7_rean2                  1.151467e-01\n",
       "tmin:income_catHigh income         1.901642e-01\n",
       "tmin:income_catLower middle income 5.674871e-02"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #Prepare GNI interaction model - Alternative Ref\n",
    "# Climate_Controls_DF$income_cat <- relevel(Climate_Controls_DF$income_cat, ref=\"Upper middle income\")\n",
    "# #GNI interaction model\n",
    "# linear_t_GNI <- felm(formula = sleep_duration_minutes ~ tmin + tmin:income_cat + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "# summary(linear_t_GNI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{Marginal Effect of Tmin: World Bank Gross National Income Category, Alternative Ref Category} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{1}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-2} \n",
      " & Base Level: Upper Middle Income \\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " tmin & $-$0.234$^{***}$ \\\\ \n",
      "  & (0.048) \\\\ \n",
      "  tmin\\_norm\\_w7 & $-$0.176$^{**}$ \\\\ \n",
      "  & (0.085) \\\\ \n",
      "  prcp & 0.482$^{***}$ \\\\ \n",
      "  & (0.121) \\\\ \n",
      "  prcp\\_norm\\_w7 & 2.124$^{*}$ \\\\ \n",
      "  & (1.086) \\\\ \n",
      "  trange & $-$0.207$^{***}$ \\\\ \n",
      "  & (0.023) \\\\ \n",
      "  trange\\_norm\\_w7 & 0.055 \\\\ \n",
      "  & (0.147) \\\\ \n",
      "  cloud\\_rean2 & 0.010$^{***}$ \\\\ \n",
      "  & (0.002) \\\\ \n",
      "  cloud\\_norm\\_7\\_rean2 & $-$0.039 \\\\ \n",
      "  & (0.024) \\\\ \n",
      "  rhum\\_rean2 & 0.001 \\\\ \n",
      "  & (0.008) \\\\ \n",
      "  rhum\\_norm\\_7\\_rean2 & 0.104$^{***}$ \\\\ \n",
      "  & (0.039) \\\\ \n",
      "  wind\\_rean2 & 0.128$^{***}$ \\\\ \n",
      "  & (0.022) \\\\ \n",
      "  wind\\_norm\\_7\\_rean2 & $-$0.295 \\\\ \n",
      "  & (0.188) \\\\ \n",
      "  tmin:income\\_catHigh income & $-$0.068 \\\\ \n",
      "  & (0.052) \\\\ \n",
      "  tmin:income\\_catLower middle income & $-$0.613$^{*}$ \\\\ \n",
      "  & (0.322) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes \\\\ \n",
      "Date FE & Yes \\\\ \n",
      "State:Month FE & Yes \\\\ \n",
      "Observations & 4,405,171 \\\\ \n",
      "R$^{2}$ & 0.284 \\\\ \n",
      "Adjusted R$^{2}$ & 0.274 \\\\ \n",
      "Residual Std. Error & 77.708 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{1}{r}{Standard errors are in parentheses and are clustered on the first admin level.} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# #Latex output\n",
    "# invisible(stargazer(linear_t_GNI,\n",
    "#           type='latex',\n",
    "#           title = \"Marginal Effect of Tmin: World Bank Gross National Income Category, Alternative Ref Category\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Base Level: Upper Middle Income'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the first admin level.'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Minimum Temperature and Daytime Napping Linear Probability Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = nap_taken ~ tmin + tmin_norm_w7 + prcp + prcp_norm_w7 +      trange + trange_norm_w7 + cloud_rean2 + cloud_norm_7_rean2 +      rhum_rean2 + rhum_norm_7_rean2 + wind_rean2 + wind_norm_7_rean2 |      userID + unique_day_no + stateyrmn | 0 | state, data = Climate_Controls_DF,      na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "     Min       1Q   Median       3Q      Max \n",
       "-0.95654 -0.14502 -0.07261 -0.01020  1.15241 \n",
       "\n",
       "Coefficients:\n",
       "                     Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin               -1.835e-04    8.119e-05  -2.260  0.02380 *  \n",
       "tmin_norm_w7        1.064e-03    2.308e-04   4.610 4.02e-06 ***\n",
       "prcp               -7.710e-05    3.493e-04  -0.221  0.82531    \n",
       "prcp_norm_w7       -4.401e-04    3.478e-03  -0.127  0.89932    \n",
       "trange             -3.531e-04    7.506e-05  -4.704 2.55e-06 ***\n",
       "trange_norm_w7      1.495e-03    4.981e-04   3.001  0.00269 ** \n",
       "cloud_rean2         1.589e-05    9.507e-06   1.671  0.09469 .  \n",
       "cloud_norm_7_rean2 -7.471e-05    9.392e-05  -0.795  0.42637    \n",
       "rhum_rean2          1.433e-05    2.258e-05   0.635  0.52564    \n",
       "rhum_norm_7_rean2  -7.765e-05    1.606e-04  -0.483  0.62884    \n",
       "wind_rean2          2.697e-04    9.675e-05   2.788  0.00531 ** \n",
       "wind_norm_7_rean2   5.384e-04    5.556e-04   0.969  0.33253    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 0.3114 on 4345352 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.1328   Adjusted R-squared: 0.1208 \n",
       "Multiple R-squared(proj model): 2.501e-05   Adjusted R-squared: -0.01374 \n",
       "F-statistic(full model, *iid*):11.12 on 59818 and 4345352 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 8.038 on 12 and 1074 DF, p-value: 1.419e-14 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# linear_t_naps <- felm(formula = nap_taken ~ tmin + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state,\n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(linear_t_naps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{S11. Nighttime Minimum Temperature and Change in Probability of Daytime Napping (Linear Probability Model)} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{1}{c}{Dependent Variable: Probability of Napping} \\\\ \n",
      "\\cline{2-2} \n",
      " & Change in Daytime Napping Probability \\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " TMIN & $-$0.0002$^{**}$ \\\\ \n",
      "  & (0.0001) \\\\ \n",
      "  TMIN.NORM & 0.001$^{***}$ \\\\ \n",
      "  & (0.0002) \\\\ \n",
      "  PRCP & $-$0.0001 \\\\ \n",
      "  & (0.0003) \\\\ \n",
      "  PRCP.NORM & $-$0.0004 \\\\ \n",
      "  & (0.003) \\\\ \n",
      "  TRANGE & $-$0.0004$^{***}$ \\\\ \n",
      "  & (0.0001) \\\\ \n",
      "  TRANGE.NORM & 0.001$^{***}$ \\\\ \n",
      "  & (0.0005) \\\\ \n",
      "  CLOUD & 0.00002$^{*}$ \\\\ \n",
      "  & (0.00001) \\\\ \n",
      "  CLOUD.NORM & $-$0.0001 \\\\ \n",
      "  & (0.0001) \\\\ \n",
      "  HUMID & 0.00001 \\\\ \n",
      "  & (0.00002) \\\\ \n",
      "  HUMID.NORM & $-$0.0001 \\\\ \n",
      "  & (0.0002) \\\\ \n",
      "  WIND & 0.0003$^{***}$ \\\\ \n",
      "  & (0.0001) \\\\ \n",
      "  WIND.NORM & 0.001 \\\\ \n",
      "  & (0.001) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes \\\\ \n",
      "Date FE & Yes \\\\ \n",
      "State:Month FE & Yes \\\\ \n",
      "Observations & 4,405,171 \\\\ \n",
      "R$^{2}$ & 0.133 \\\\ \n",
      "Adjusted R$^{2}$ & 0.121 \\\\ \n",
      "Residual Std. Error & 0.311 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{1}{r}{Standard errors are in parentheses and are clustered on the state admin level} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# invisible(stargazer(linear_t_naps,\n",
    "#           type='latex',\n",
    "#           title = \"S11. Nighttime Minimum Temperature and Change in Probability of Daytime Napping (Linear Probability Model)\",\n",
    "#           out='linear_t_naps.tex',\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Change in Daytime Napping Probability'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Probability of Napping',\n",
    "#           covariate.labels = c(\"TMIN\", \"TMIN.NORM\", \"PRCP\", \"PRCP.NORM\", \"TRANGE\", \"TRANGE.NORM\", \"CLOUD\", \"CLOUD.NORM\", \"HUMID\", \"HUMID.NORM\", \"WIND\", \"WIND.NORM\"),\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the state admin level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Alternative Time and Location Controls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmin + tmin_norm_w7 +      prcp + prcp_norm_w7 + trange + trange_norm_w7 + cloud_rean2 +      cloud_norm_7_rean2 + rhum_rean2 + rhum_norm_7_rean2 + wind_rean2 +      wind_norm_7_rean2 | userID + unique_day_no + statewk | 0 |      state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-366.24  -48.85   -2.18   45.52  409.09 \n",
       "\n",
       "Coefficients:\n",
       "                    Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin               -0.329376     0.026652 -12.359  < 2e-16 ***\n",
       "tmin_norm_w7       -0.296105     0.112455  -2.633  0.00846 ** \n",
       "prcp                0.526511     0.129064   4.079 4.51e-05 ***\n",
       "prcp_norm_w7        2.042539     1.652325   1.236  0.21640    \n",
       "trange             -0.229557     0.028753  -7.984 1.42e-15 ***\n",
       "trange_norm_w7     -0.141874     0.189086  -0.750  0.45307    \n",
       "cloud_rean2         0.013203     0.002658   4.968 6.76e-07 ***\n",
       "cloud_norm_7_rean2 -0.001480     0.033735  -0.044  0.96501    \n",
       "rhum_rean2          0.007301     0.008342   0.875  0.38149    \n",
       "rhum_norm_7_rean2   0.143309     0.046808   3.062  0.00220 ** \n",
       "wind_rean2          0.143432     0.022010   6.517 7.20e-11 ***\n",
       "wind_norm_7_rean2  -0.566519     0.243032  -2.331  0.01975 *  \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.66 on 4304979 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2918   Adjusted R-squared: 0.2753 \n",
       "Multiple R-squared(proj model): 0.0001491   Adjusted R-squared: -0.02312 \n",
       "F-statistic(full model, *iid*): 17.7 on 100191 and 4304979 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 39.06 on 12 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #Admin1(State)-Week FE\n",
    "# linear_t_state_week <- felm(formula = sleep_duration_minutes ~ tmin + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2  \n",
    "#                 | userID + unique_day_no + statewk \n",
    "#                 | 0 \n",
    "#                 | state,\n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(linear_t_state_week)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmin + tmin_norm_w7 +      prcp + prcp_norm_w7 + trange + trange_norm_w7 + cloud_rean2 +      cloud_norm_7_rean2 + rhum_rean2 + rhum_norm_7_rean2 + wind_rean2 +      wind_norm_7_rean2 | userID + unique_day_no + countryyrmn |      0 | country, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-389.43  -49.31   -2.38   45.90  405.50 \n",
       "\n",
       "Coefficients:\n",
       "                    Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin               -0.288497     0.043318  -6.660 2.74e-11 ***\n",
       "tmin_norm_w7       -0.030446     0.102299  -0.298 0.765991    \n",
       "prcp                0.462283     0.137760   3.356 0.000792 ***\n",
       "prcp_norm_w7        2.637847     1.433183   1.841 0.065687 .  \n",
       "trange             -0.208846     0.018093 -11.543  < 2e-16 ***\n",
       "trange_norm_w7      0.195078     0.290073   0.673 0.501257    \n",
       "cloud_rean2         0.009284     0.002741   3.387 0.000705 ***\n",
       "cloud_norm_7_rean2 -0.073190     0.013251  -5.523 3.33e-08 ***\n",
       "rhum_rean2          0.003103     0.010970   0.283 0.777291    \n",
       "rhum_norm_7_rean2   0.112746     0.027522   4.097 4.19e-05 ***\n",
       "wind_rean2          0.134311     0.040575   3.310 0.000932 ***\n",
       "wind_norm_7_rean2  -0.024833     0.231099  -0.107 0.914428    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.8 on 4359919 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model):  0.28   Adjusted R-squared: 0.2726 \n",
       "Multiple R-squared(proj model): 0.0001664   Adjusted R-squared: -0.01021 \n",
       "F-statistic(full model, *iid*):37.48 on 45251 and 4359919 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 58.48 on 12 and 67 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #Country-Month FE\n",
    "# linear_t_country_month <- felm(formula = sleep_duration_minutes ~ tmin + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2  \n",
    "#                 | userID + unique_day_no + countryyrmn \n",
    "#                 | 0 \n",
    "#                 | country,\n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(linear_t_country_month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{S9. Time and Location Controls} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lccc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{3}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-4} \n",
      " & State-Week Control & State-Month Control & Country-Month Control \\\\ \n",
      "\\\\[-1.8ex] & (1) & (2) & (3)\\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " TMIN & $-$0.329$^{***}$ & $-$0.295$^{***}$ & $-$0.288$^{***}$ \\\\ \n",
      "  & (0.027) & (0.027) & (0.043) \\\\ \n",
      "  TMIN.NORM & $-$0.296$^{***}$ & $-$0.178$^{**}$ & $-$0.030 \\\\ \n",
      "  & (0.112) & (0.085) & (0.102) \\\\ \n",
      "  PRCP & 0.527$^{***}$ & 0.484$^{***}$ & 0.462$^{***}$ \\\\ \n",
      "  & (0.129) & (0.120) & (0.138) \\\\ \n",
      "  PRCP.NORM & 2.043 & 2.118$^{*}$ & 2.638$^{*}$ \\\\ \n",
      "  & (1.652) & (1.085) & (1.433) \\\\ \n",
      "  TRANGE & $-$0.230$^{***}$ & $-$0.208$^{***}$ & $-$0.209$^{***}$ \\\\ \n",
      "  & (0.029) & (0.023) & (0.018) \\\\ \n",
      "  TRANGE.NORM & $-$0.142 & 0.055 & 0.195 \\\\ \n",
      "  & (0.189) & (0.147) & (0.290) \\\\ \n",
      "  CLOUD & 0.013$^{***}$ & 0.010$^{***}$ & 0.009$^{***}$ \\\\ \n",
      "  & (0.003) & (0.002) & (0.003) \\\\ \n",
      "  CLOUD.NORM & $-$0.001 & $-$0.039 & $-$0.073$^{***}$ \\\\ \n",
      "  & (0.034) & (0.024) & (0.013) \\\\ \n",
      "  HUMID & 0.007 & 0.001 & 0.003 \\\\ \n",
      "  & (0.008) & (0.008) & (0.011) \\\\ \n",
      "  HUMID.NORM & 0.143$^{***}$ & 0.104$^{***}$ & 0.113$^{***}$ \\\\ \n",
      "  & (0.047) & (0.040) & (0.028) \\\\ \n",
      "  WIND & 0.143$^{***}$ & 0.128$^{***}$ & 0.134$^{***}$ \\\\ \n",
      "  & (0.022) & (0.022) & (0.041) \\\\ \n",
      "  WIND.NORM & $-$0.567$^{**}$ & $-$0.296 & $-$0.025 \\\\ \n",
      "  & (0.243) & (0.188) & (0.231) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes & Yes & Yes \\\\ \n",
      "Date FE & Yes & Yes & Yes \\\\ \n",
      "State:Week FE & Yes & No & No \\\\ \n",
      "State:Month FE & No & Yes & No \\\\ \n",
      "Country:Month FE & No & No & Yes \\\\ \n",
      "Observations & 4,405,171 & 4,405,171 & 4,405,171 \\\\ \n",
      "R$^{2}$ & 0.292 & 0.284 & 0.280 \\\\ \n",
      "Adjusted R$^{2}$ & 0.275 & 0.274 & 0.273 \\\\ \n",
      "Residual Std. Error & 77.656 & 77.708 & 77.804 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{3}{r}{Standard errors are in parentheses and are clustered on the state admin level for the State-Week and State-Month models and on the country admin level for the Country-Month model} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# invisible(stargazer(linear_t_state_week, linear_t, linear_t_country_month,\n",
    "#           type='latex',\n",
    "#           title = \"Time and Location Controls\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('State-Week Control', 'State-Month Control', 'Country-Month Control'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           covariate.labels = c(\"TMIN\", \"TMIN.NORM\", \"PRCP\", \"PRCP.NORM\", \"TRANGE\", \"TRANGE.NORM\", \"CLOUD\", \"CLOUD.NORM\", \"HUMID\", \"HUMID.NORM\", \"WIND\", \"WIND.NORM\"),\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Week FE\", \"Yes\", \"No\", \"No\"),\n",
    "#                            c(\"State:Month FE\", \"No\", \"Yes\", \"No\"),\n",
    "#                            c(\"Country:Month FE\", \"No\", \"No\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the state admin level for the State-Week and State-Month models and on the country admin level for the Country-Month model'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Alternative Sleep Timing Filters for Inclusion in Sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Robustness Checks/Sensitivity Analyses\n",
    "# #Narrow time filters (Walch, Altoff)\n",
    "#Climate_Controls_DF_Walch <- subset(Climate_Controls_DF, (onset > 19 | onset < 3))\n",
    "#Climate_Controls_DF_Walch <- subset(Climate_Controls_DF_Walch, (offset > 3 & offset < 11))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmin + tmin_norm_w7 +      prcp + prcp_norm_w7 + trange + trange_norm_w7 + cloud_rean2 +      cloud_norm_7_rean2 + rhum_rean2 + rhum_norm_7_rean2 + wind_rean2 +      wind_norm_7_rean2 | userID + unique_day_no + stateyrmn |      0 | state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-380.86  -47.83   -2.41   44.50  409.03 \n",
       "\n",
       "Coefficients:\n",
       "                     Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin               -0.2914283    0.0276649 -10.534  < 2e-16 ***\n",
       "tmin_norm_w7       -0.1864326    0.0818031  -2.279   0.0227 *  \n",
       "prcp                0.5502463    0.1337849   4.113 3.91e-05 ***\n",
       "prcp_norm_w7        2.1963630    1.0423191   2.107   0.0351 *  \n",
       "trange             -0.2131142    0.0222820  -9.564  < 2e-16 ***\n",
       "trange_norm_w7      0.0471143    0.1579406   0.298   0.7655    \n",
       "cloud_rean2         0.0111131    0.0024027   4.625 3.74e-06 ***\n",
       "cloud_norm_7_rean2 -0.0329708    0.0256466  -1.286   0.1986    \n",
       "rhum_rean2          0.0006596    0.0079771   0.083   0.9341    \n",
       "rhum_norm_7_rean2   0.0810719    0.0366949   2.209   0.0272 *  \n",
       "wind_rean2          0.1521293    0.0208419   7.299 2.90e-13 ***\n",
       "wind_norm_7_rean2  -0.3051840    0.1722911  -1.771   0.0765 .  \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 75.94 on 4115760 degrees of freedom\n",
       "  (2852445 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.3024   Adjusted R-squared: 0.2923 \n",
       "Multiple R-squared(proj model): 0.0001729   Adjusted R-squared: -0.01431 \n",
       "F-statistic(full model, *iid*):29.94 on 59601 and 4115760 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model):  45.8 on 12 and 1072 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #Walch Narrow Time Filter robustness check\n",
    "# linear_t_narrow <- felm(formula = sleep_duration_minutes ~ tmin + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(linear_t_narrow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# invisible(stargazer(linear_t, linear_t_narrow,\n",
    "#           type='latex',\n",
    "#           title = \"Table S31: Alternative Sleep Timing Filters for Inclusion in Sample\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('19:00<onset<08:00 and 00:00<offset<15:00', 'Walch et al.(2016): 19<onset<3 & 3<offset<11'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the 1st-level admin division'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Alternative Minimum Percentage of Nights with Wearable Device Use for Inclusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Robustness Checks/Sensitivity Analyses\n",
    "# Wore wristband at least 50% of the time\n",
    "# Climate_Controls_DF_50percent <- subset(Climate_Controls_DF_Sub, (percent_days_w_sleepentries >= .50))\n",
    "\n",
    "# #Wore wristband at least 75% of the time\n",
    "# Climate_Controls_DF_75percent <- subset(Climate_Controls_DF, (percent_days_w_sleepentries >= .75))\n",
    "\n",
    "# #Wore wristband at least 85% of the time\n",
    "# Climate_Controls_DF_85percent <- subset(Climate_Controls_DF, (percent_days_w_sleepentries >= .85))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmin + tmin_norm_w7 +      prcp + prcp_norm_w7 + trange + trange_norm_w7 + cloud_rean2 +      cloud_norm_7_rean2 + rhum_rean2 + rhum_norm_7_rean2 + wind_rean2 +      wind_norm_7_rean2 | userID + unique_day_no + stateyrmn |      0 | state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-360.82  -47.77   -2.01   44.82  384.44 \n",
       "\n",
       "Coefficients:\n",
       "                     Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin               -0.3073915    0.0265804 -11.565  < 2e-16 ***\n",
       "tmin_norm_w7       -0.1110149    0.0913135  -1.216    0.224    \n",
       "prcp                0.5217378    0.1328920   3.926 8.64e-05 ***\n",
       "prcp_norm_w7        1.0629955    1.1525826   0.922    0.356    \n",
       "trange             -0.2300762    0.0246197  -9.345  < 2e-16 ***\n",
       "trange_norm_w7      0.0052651    0.1562173   0.034    0.973    \n",
       "cloud_rean2         0.0113275    0.0025914   4.371 1.24e-05 ***\n",
       "cloud_norm_7_rean2 -0.0419668    0.0290002  -1.447    0.148    \n",
       "rhum_rean2          0.0008019    0.0085372   0.094    0.925    \n",
       "rhum_norm_7_rean2   0.1362288    0.0513698   2.652    0.008 ** \n",
       "wind_rean2          0.1380746    0.0246575   5.600 2.15e-08 ***\n",
       "wind_norm_7_rean2  -0.2102773    0.1736340  -1.211    0.226    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 76.37 on 3129672 degrees of freedom\n",
       "  (2081420 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2857   Adjusted R-squared: 0.2762 \n",
       "Multiple R-squared(proj model): 0.0001743   Adjusted R-squared: -0.01325 \n",
       "F-statistic(full model, *iid*):29.81 on 42009 and 3129672 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 32.67 on 12 and 985 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #50% robustness\n",
    "# linear_t_50percent <- felm(formula = sleep_duration_minutes ~ tmin + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(linear_t_50percent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmin + tmin_norm_w7 +      prcp + prcp_norm_w7 + trange + trange_norm_w7 + cloud_rean2 +      cloud_norm_7_rean2 + rhum_rean2 + rhum_norm_7_rean2 + wind_rean2 +      wind_norm_7_rean2 | userID + unique_day_no + stateyrmn |      0 | state, data = Climate_Controls_DF_75percent, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-357.54  -44.66   -1.44   42.53  385.56 \n",
       "\n",
       "Coefficients:\n",
       "                    Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin               -0.316286     0.037140  -8.516  < 2e-16 ***\n",
       "tmin_norm_w7        0.028419     0.131618   0.216    0.829    \n",
       "prcp                0.627159     0.143019   4.385 1.16e-05 ***\n",
       "prcp_norm_w7        0.382491     1.717081   0.223    0.824    \n",
       "trange             -0.234744     0.035309  -6.648 2.97e-11 ***\n",
       "trange_norm_w7      0.061668     0.248532   0.248    0.804    \n",
       "cloud_rean2         0.015058     0.003643   4.134 3.57e-05 ***\n",
       "cloud_norm_7_rean2 -0.049187     0.039094  -1.258    0.208    \n",
       "rhum_rean2          0.010607     0.010432   1.017    0.309    \n",
       "rhum_norm_7_rean2   0.111096     0.083618   1.329    0.184    \n",
       "wind_rean2          0.138070     0.034808   3.967 7.29e-05 ***\n",
       "wind_norm_7_rean2  -0.170534     0.346954  -0.492    0.623    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 73.19 on 1335457 degrees of freedom\n",
       "  (835337 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2928   Adjusted R-squared: 0.2811 \n",
       "Multiple R-squared(proj model): 0.0002104   Adjusted R-squared: -0.01639 \n",
       "F-statistic(full model, *iid*):24.93 on 22177 and 1335457 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 21.15 on 12 and 824 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #75% robustness\n",
    "# linear_t_75percent <- felm(formula = sleep_duration_minutes ~ tmin + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF_75percent, na.action = na.omit) \n",
    "\n",
    "# summary(linear_t_75percent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmin + tmin_norm_w7 +      prcp + prcp_norm_w7 + trange + trange_norm_w7 + cloud_rean2 +      cloud_norm_7_rean2 + rhum_rean2 + rhum_norm_7_rean2 + wind_rean2 +      wind_norm_7_rean2 | userID + unique_day_no + stateyrmn |      0 | state, data = Climate_Controls_DF_85percent, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-336.41  -41.82   -1.17   40.15  348.68 \n",
       "\n",
       "Coefficients:\n",
       "                    Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin               -0.297407     0.050803  -5.854 4.80e-09 ***\n",
       "tmin_norm_w7        0.145818     0.179435   0.813 0.416419    \n",
       "prcp                0.802023     0.195716   4.098 4.17e-05 ***\n",
       "prcp_norm_w7        0.421871     2.158452   0.195 0.845040    \n",
       "trange             -0.220963     0.048972  -4.512 6.42e-06 ***\n",
       "trange_norm_w7      0.084407     0.331957   0.254 0.799287    \n",
       "cloud_rean2         0.014833     0.004784   3.100 0.001932 ** \n",
       "cloud_norm_7_rean2 -0.045992     0.051721  -0.889 0.373873    \n",
       "rhum_rean2          0.015288     0.014052   1.088 0.276608    \n",
       "rhum_norm_7_rean2   0.152099     0.101803   1.494 0.135164    \n",
       "wind_rean2          0.134288     0.039036   3.440 0.000582 ***\n",
       "wind_norm_7_rean2  -0.593428     0.363416  -1.633 0.102488    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 70.09 on 594143 degrees of freedom\n",
       "  (354417 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.3075   Adjusted R-squared: 0.292 \n",
       "Multiple R-squared(proj model): 0.0002427   Adjusted R-squared: -0.02212 \n",
       "F-statistic(full model, *iid*):19.85 on 13291 and 594143 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 12.92 on 12 and 651 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #85% robustness\n",
    "# linear_t_85percent <- felm(formula = sleep_duration_minutes ~ tmin + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF_85percent, na.action = na.omit) \n",
    "\n",
    "# summary(linear_t_85percent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# invisible(stargazer(linear_t, linear_t_50percent, linear_t_75percent, linear_t_85percent,\n",
    "#           type='latex',\n",
    "#           title = \"Minimum % of Total Nights with Wristband Use\",\n",
    "#           out='linear_t_percent.tex',\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('>25% of nights', '>50% of nights', '>75% of nights', '>85% of nights'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           covariate.labels = c(\"TMIN\", \"TMIN.NORM\", \"PRCP\", \"PRCP.NORM\", \"TRANGE\", \"TRANGE.NORM\", \"CLOUD\", \"CLOUD.NORM\", \"HUMID\", \"HUMID.NORM\", \"WIND\", \"WIND.NORM\"),\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the state admin level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Alternative Minimum Number of Nights of Wearable Device Use for Inclusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Robustness Checks/Sensitivity Analyses\n",
    "# #at least 56 nights with sleep entries\n",
    "# Climate_Controls_DF_56nights <- subset(Climate_Controls_DF, (count >= 56))\n",
    "\n",
    "# #at least 84 nights with sleep entries\n",
    "# Climate_Controls_DF_84nights <- subset(Climate_Controls_DF, (count >= 84))\n",
    "\n",
    "# #at least 112 nights with sleep entries\n",
    "# Climate_Controls_DF_112nights <- subset(Climate_Controls_DF, (count >= 112))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmin + tmin_norm_w7 +      prcp + prcp_norm_w7 + trange + trange_norm_w7 + cloud_rean2 +      cloud_norm_7_rean2 + rhum_rean2 + rhum_norm_7_rean2 + wind_rean2 +      wind_norm_7_rean2 | userID + unique_day_no + stateyrmn |      0 | state, data = Climate_Controls_DF_56nights, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-384.44  -49.10   -2.35   45.70  407.70 \n",
       "\n",
       "Coefficients:\n",
       "                    Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin               -0.295861     0.027206 -10.875  < 2e-16 ***\n",
       "tmin_norm_w7       -0.173736     0.088076  -1.973 0.048544 *  \n",
       "prcp                0.466157     0.120950   3.854 0.000116 ***\n",
       "prcp_norm_w7        2.338937     1.084399   2.157 0.031014 *  \n",
       "trange             -0.211853     0.022668  -9.346  < 2e-16 ***\n",
       "trange_norm_w7      0.028890     0.145170   0.199 0.842257    \n",
       "cloud_rean2         0.010264     0.002407   4.264 2.01e-05 ***\n",
       "cloud_norm_7_rean2 -0.042063     0.023171  -1.815 0.069473 .  \n",
       "rhum_rean2          0.000574     0.007570   0.076 0.939562    \n",
       "rhum_norm_7_rean2   0.109889     0.041059   2.676 0.007442 ** \n",
       "wind_rean2          0.135682     0.021827   6.216 5.10e-10 ***\n",
       "wind_norm_7_rean2  -0.292003     0.190707  -1.531 0.125730    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.6 on 4152436 degrees of freedom\n",
       "  (2835415 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2833   Adjusted R-squared: 0.2744 \n",
       "Multiple R-squared(proj model): 0.0001572   Adjusted R-squared: -0.0123 \n",
       "F-statistic(full model, *iid*):31.73 on 51736 and 4152436 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 42.97 on 12 and 1053 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #56nights robustness\n",
    "# linear_t_56nights <- felm(formula = sleep_duration_minutes ~ tmin + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF_56nights, na.action = na.omit) \n",
    "\n",
    "# summary(linear_t_56nights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmin + tmin_norm_w7 +      prcp + prcp_norm_w7 + trange + trange_norm_w7 + cloud_rean2 +      cloud_norm_7_rean2 + rhum_rean2 + rhum_norm_7_rean2 + wind_rean2 +      wind_norm_7_rean2 | userID + unique_day_no + stateyrmn |      0 | state, data = Climate_Controls_DF_84nights, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-384.58  -49.03   -2.37   45.61  407.81 \n",
       "\n",
       "Coefficients:\n",
       "                    Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin               -0.287300     0.026947 -10.662  < 2e-16 ***\n",
       "tmin_norm_w7       -0.131184     0.088595  -1.481 0.138679    \n",
       "prcp                0.528808     0.120921   4.373 1.22e-05 ***\n",
       "prcp_norm_w7        2.227521     1.125018   1.980 0.047705 *  \n",
       "trange             -0.216049     0.023417  -9.226  < 2e-16 ***\n",
       "trange_norm_w7      0.057018     0.146987   0.388 0.698081    \n",
       "cloud_rean2         0.009392     0.002416   3.888 0.000101 ***\n",
       "cloud_norm_7_rean2 -0.050744     0.023802  -2.132 0.033013 *  \n",
       "rhum_rean2          0.001100     0.007712   0.143 0.886558    \n",
       "rhum_norm_7_rean2   0.122864     0.044210   2.779 0.005451 ** \n",
       "wind_rean2          0.130610     0.022239   5.873 4.28e-09 ***\n",
       "wind_norm_7_rean2  -0.238532     0.189556  -1.258 0.208257    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.48 on 3914149 degrees of freedom\n",
       "  (2642890 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2826   Adjusted R-squared: 0.2743 \n",
       "Multiple R-squared(proj model): 0.0001536   Adjusted R-squared: -0.01148 \n",
       "F-statistic(full model, *iid*):33.87 on 45537 and 3914149 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 42.32 on 12 and 1027 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #84nights robustness\n",
    "# linear_t_84nights <- felm(formula = sleep_duration_minutes ~ tmin + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF_84nights, na.action = na.omit) \n",
    "\n",
    "# summary(linear_t_84nights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmin + tmin_norm_w7 +      prcp + prcp_norm_w7 + trange + trange_norm_w7 + cloud_rean2 +      cloud_norm_7_rean2 + rhum_rean2 + rhum_norm_7_rean2 + wind_rean2 +      wind_norm_7_rean2 | userID + unique_day_no + stateyrmn |      0 | state, data = Climate_Controls_DF_112nights, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-384.80  -48.94   -2.39   45.49  407.87 \n",
       "\n",
       "Coefficients:\n",
       "                    Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin               -0.282195     0.027469 -10.273  < 2e-16 ***\n",
       "tmin_norm_w7       -0.121463     0.091626  -1.326  0.18496    \n",
       "prcp                0.523014     0.124152   4.213 2.52e-05 ***\n",
       "prcp_norm_w7        2.266860     1.168295   1.940  0.05234 .  \n",
       "trange             -0.214899     0.024053  -8.934  < 2e-16 ***\n",
       "trange_norm_w7      0.070939     0.149765   0.474  0.63574    \n",
       "cloud_rean2         0.009491     0.002454   3.868  0.00011 ***\n",
       "cloud_norm_7_rean2 -0.058524     0.024471  -2.392  0.01678 *  \n",
       "rhum_rean2          0.002174     0.007847   0.277  0.78178    \n",
       "rhum_norm_7_rean2   0.115128     0.045877   2.510  0.01209 *  \n",
       "wind_rean2          0.137918     0.023851   5.783 7.36e-09 ***\n",
       "wind_norm_7_rean2  -0.212645     0.188580  -1.128  0.25949    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.33 on 3655946 degrees of freedom\n",
       "  (2440203 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2821   Adjusted R-squared: 0.2741 \n",
       "Multiple R-squared(proj model): 0.0001522   Adjusted R-squared: -0.01096 \n",
       "F-statistic(full model, *iid*):35.34 on 40638 and 3655946 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 40.19 on 12 and 1005 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #112nights robustness\n",
    "# linear_t_112nights <- felm(formula = sleep_duration_minutes ~ tmin + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF_112nights, na.action = na.omit) \n",
    "\n",
    "# summary(linear_t_112nights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# invisible(stargazer(linear_t, linear_t_56nights, linear_t_84nights, linear_t_112nights,\n",
    "#           type='latex',\n",
    "#           title = \"Minimum # of Nights of Wristband Use Inclusion Criteria\",\n",
    "#           out='linear_t_nights.tex',\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('>28 nights', '>56 nights', '>84 nights', '>112 nights'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           covariate.labels = c(\"TMIN\", \"TMIN.NORM\", \"PRCP\", \"PRCP.NORM\", \"TRANGE\", \"TRANGE.NORM\", \"CLOUD\", \"CLOUD.NORM\", \"HUMID\", \"HUMID.NORM\", \"WIND\", \"WIND.NORM\"),\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the state admin level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Alternative (Binned) Climate Normals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Binned climate **norms** GHCND\n",
    "# flex_t_extend_GHCND <- felm(formula = sleep_duration_minutes ~ CUTTMIN + CUTTMIN.NORM1981_2010 + \n",
    "#                                           CUTPRCP + CUTPRCP.NORM1981_2010 + \n",
    "#                                           CUTTRANGE + CUTTRANGE.NORM1981_2010 + \n",
    "#                                           CUTCLOUD_rean2 + CUTCLOUD.NORM1981_2010_rean2 + \n",
    "#                                           CUTRHUM_rean2 + CUTRHUM.NORM1981_2010_rean2 + \n",
    "#                                           CUTWIND_rean2 + CUTWIND.NORM1981_2010_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(flex_t_extend_GHCND)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Create Reanalysis 2 dataset with matched weather variable names\n",
    "# Climate_Controls_R2_DF <- Climate_Controls_DF\n",
    "# Climate_Controls_R2_DF$CUTTMIN <- Climate_Controls_R2_DF$CUTTMIN_rean2\n",
    "# Climate_Controls_R2_DF$CUTTMIN.NORM1981_2010 <- Climate_Controls_R2_DF$CUTTMIN.NORM1981_2010_rean2\n",
    "\n",
    "# Climate_Controls_R2_DF$CUTPRCP <- Climate_Controls_R2_DF$CUTPRCP_rean2\n",
    "# Climate_Controls_R2_DF$CUTPRCP.NORM1981_2010 <- Climate_Controls_R2_DF$CUTPRCP.NORM1981_2010_rean2\n",
    "\n",
    "# Climate_Controls_R2_DF$CUTTRANGE <- Climate_Controls_R2_DF$CUTTRANGE_rean2\n",
    "# Climate_Controls_R2_DF$CUTTRANGE.NORM1981_2010 <- Climate_Controls_R2_DF$CUTTRANGE.NORM1981_2010_rean2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Binned climate **norms** REAN2\n",
    "# flex_t_extend_REAN2 <- felm(formula = sleep_duration_minutes ~ CUTTMIN + CUTTMIN.NORM1981_2010 + \n",
    "#                                           CUTPRCP + CUTPRCP.NORM1981_2010 + \n",
    "#                                           CUTTRANGE + CUTTRANGE.NORM1981_2010 + \n",
    "#                                           CUTCLOUD_rean2 + CUTCLOUD.NORM1981_2010_rean2 + \n",
    "#                                           CUTRHUM_rean2 + CUTRHUM.NORM1981_2010_rean2 + \n",
    "#                                           CUTWIND_rean2 + CUTWIND.NORM1981_2010_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_R2_DF, na.action = na.omit) \n",
    "\n",
    "# summary(flex_t_extend_REAN2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# invisible(stargazer(flex_t_extend_GHCND, flex_t_extend_REAN2,\n",
    "#           type='latex',\n",
    "#           title = \"Flexible TMIN Binned Climate Normal Regressions\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Flexible TMIN GHCND','Flexible TMIN Reanalysis 2'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\",\"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Admin_2:Month FE\", \"Yes\", \"Yes\",\"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the Admin 2 level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Acclimatization Test I: Marginal Effect by Summer Month (Intra-Annual)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Acclimatization Test Prep\n",
    "# #Select First and Last Summer Months\n",
    "# setDT(Climate_Controls_DF)\n",
    "\n",
    "# #convert southern hemisphere months to equivalent seasonal month #s for northern hemisphere\n",
    "\n",
    "# Climate_Controls_DF[,month_global := month]\n",
    "\n",
    "# #First month of summer\n",
    "# Climate_Controls_DF[(country==\"Argentina\" & month==12)  | (country==\"Australia\" & month==12) \n",
    "#                     | (country==\"Chile\" & month==12) | (country==\"Indonesia\" & month==12)\n",
    "#                     | (country==\"New Zealand\" & month==12) | (country==\"South Africa\" & month==12), \n",
    "#                    month_global := 6]\n",
    "# #Last month of summer\n",
    "# Climate_Controls_DF[(country==\"Argentina\" & month==2)  | (country==\"Australia\" & month==2) \n",
    "#                     | (country==\"Chile\" & month==2) | (country==\"Indonesia\" & month==2)\n",
    "#                     | (country==\"New Zealand\" & month==2) | (country==\"South Africa\" & month==2), \n",
    "#                    month_global := 8]\n",
    "\n",
    "# #Correct winter months\n",
    "# Climate_Controls_DF[(country==\"Argentina\" & month==6)  | (country==\"Australia\" & month==6) \n",
    "#                     | (country==\"Chile\" & month==6) | (country==\"Indonesia\" & month==6)\n",
    "#                     | (country==\"New Zealand\" & month==6) | (country==\"South Africa\" & month==6), \n",
    "#                    month_global := 12]\n",
    "# #Last month of winter\n",
    "# Climate_Controls_DF[(country==\"Argentina\" & month==8)  | (country==\"Australia\" & month==8) \n",
    "#                     | (country==\"Chile\" & month==8) | (country==\"Indonesia\" & month==8)\n",
    "#                     | (country==\"New Zealand\" & month==8) | (country==\"South Africa\" & month==8), \n",
    "#                    month_global := 2]\n",
    "\n",
    "# Climate_Controls_DF[, month := month_global]\n",
    "\n",
    "# #Subset data by first and last months of summer\n",
    "# Climate_Controls_DF <- Climate_Controls_DF[month==6 | month==8]\n",
    "\n",
    "# First <- Climate_Controls_DF[month == 6]\n",
    "# First[,month:= as.factor(as.character(month))]\n",
    "\n",
    "# Last <- Climate_Controls_DF[month == 8]\n",
    "# Last[,month:= as.factor(as.character(month))]\n",
    "\n",
    "# Climate_Controls_DF[,month:= as.factor(as.character(month))]\n",
    "\n",
    "# #check that dates are correct\n",
    "# Climate_Controls_DF[(country==\"Argentina\" & month==6),.(date)]\n",
    "\n",
    "# setDF(First)\n",
    "# setDF(Last)\n",
    "\n",
    "# #Convert to DF for modelling\n",
    "# setDF(Climate_Controls_DF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmin + tmin:month + tmin_norm_w7 +      prcp + prcp_norm_w7 + trange + trange_norm_w7 + cloud_rean2 +      cloud_norm_7_rean2 + rhum_rean2 + rhum_norm_7_rean2 + wind_rean2 +      wind_norm_7_rean2 | userID + unique_day_no + state | 0 |      state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-341.91  -46.98   -2.30   43.30  370.65 \n",
       "\n",
       "Coefficients:\n",
       "                    Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin               -0.831068     0.069043 -12.037  < 2e-16 ***\n",
       "tmin_norm_w7        0.218871     0.156773   1.396 0.162683    \n",
       "prcp                0.063089     0.211142   0.299 0.765093    \n",
       "prcp_norm_w7        3.718061     2.205773   1.686 0.091873 .  \n",
       "trange             -0.571055     0.059695  -9.566  < 2e-16 ***\n",
       "trange_norm_w7     -0.245278     0.323912  -0.757 0.448908    \n",
       "cloud_rean2         0.021603     0.005757   3.752 0.000175 ***\n",
       "cloud_norm_7_rean2  0.019191     0.046200   0.415 0.677863    \n",
       "rhum_rean2         -0.009086     0.014664  -0.620 0.535497    \n",
       "rhum_norm_7_rean2  -0.022585     0.085708  -0.264 0.792154    \n",
       "wind_rean2          0.143653     0.060486   2.375 0.017551 *  \n",
       "wind_norm_7_rean2   0.200399     0.540894   0.370 0.711013    \n",
       "tmin:month8        -0.136714     0.066076  -2.069 0.038543 *  \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 75.52 on 648918 degrees of freedom\n",
       "  (465136 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.3137   Adjusted R-squared: 0.2828 \n",
       "Multiple R-squared(proj model): 0.001085   Adjusted R-squared: -0.04386 \n",
       "F-statistic(full model, *iid*):10.16 on 29199 and 648918 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 39.93 on 13 and 843 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #Intra-Annual Acclimatization Test (first vs. last month of summer)\n",
    "# linear_t_acc <- felm(formula = sleep_duration_minutes ~ tmin + tmin:month + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + state \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "# summary(linear_t_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{Acclimatization Test: Marg. Effect of Summer Month} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{1}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-2} \n",
      " & Base Level: First Summer Month \\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " TMIN & $-$0.831$^{***}$ \\\\ \n",
      "  & (0.069) \\\\ \n",
      "  TMIN.NORM & 0.219 \\\\ \n",
      "  & (0.157) \\\\ \n",
      "  PRCP & 0.063 \\\\ \n",
      "  & (0.211) \\\\ \n",
      "  PRCP.NORM & 3.718$^{*}$ \\\\ \n",
      "  & (2.206) \\\\ \n",
      "  TRANGE & $-$0.571$^{***}$ \\\\ \n",
      "  & (0.060) \\\\ \n",
      "  TRANGE.NORM & $-$0.245 \\\\ \n",
      "  & (0.324) \\\\ \n",
      "  CLOUD & 0.022$^{***}$ \\\\ \n",
      "  & (0.006) \\\\ \n",
      "  CLOUD.NORM & 0.019 \\\\ \n",
      "  & (0.046) \\\\ \n",
      "  HUMID & $-$0.009 \\\\ \n",
      "  & (0.015) \\\\ \n",
      "  HUMID.NORM & $-$0.023 \\\\ \n",
      "  & (0.086) \\\\ \n",
      "  WIND & 0.144$^{**}$ \\\\ \n",
      "  & (0.060) \\\\ \n",
      "  WIND.NORM & 0.200 \\\\ \n",
      "  & (0.541) \\\\ \n",
      "  TMIN:LAST_SUMMER_MONTH & $-$0.137$^{**}$ \\\\ \n",
      "  & (0.066) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes \\\\ \n",
      "Date FE & Yes \\\\ \n",
      "State:Month FE & Yes \\\\ \n",
      "Observations & 678,118 \\\\ \n",
      "R$^{2}$ & 0.314 \\\\ \n",
      "Adjusted R$^{2}$ & 0.283 \\\\ \n",
      "Residual Std. Error & 75.524 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{1}{r}{Standard errors are in parentheses and are clustered on the state admin level.} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# #Latex output\n",
    "# invisible(stargazer(linear_t_acc,\n",
    "#           type='latex',\n",
    "#           title = \"Acclimatization Test: Marg. Effect of Summer Month\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Base Level: First Summer Month'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           covariate.labels = c(\"TMIN\", \"TMIN.NORM\", \"PRCP\", \"PRCP.NORM\", \"TRANGE\", \"TRANGE.NORM\", \"CLOUD\", \"CLOUD.NORM\", \"HUMID\", \"HUMID.NORM\", \"WIND\", \"WIND.NORM\", \"TMIN:LAST_SUMMER_MONTH\"),\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the state admin level.'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Acclimatization Test 2: Distributed Lag Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #load continuous REAN2 daily weather and climate series data\n",
    "# sleepa<-readRDS(file=\"sleep_lag_REAN2_admin_2.rds\")\n",
    "\n",
    "# #prep vars for merge\n",
    "# setDT(Climate_Controls_DF)\n",
    "# Climate_Controls_DF[,unique_day_no:=as.character(unique_day_no)]\n",
    "# Climate_Controls_DF[,state:=as.character(state)]\n",
    "# Climate_Controls_DF[,country:=as.character(country)]\n",
    "\n",
    "# #merge data\n",
    "# nrow(Climate_Controls_DF)\n",
    "# Climate_Controls_DF<-merge(sleepa, Climate_Controls_DF, by=c(\"ID\",\"unique_day_no\"),all.x=T)\n",
    "# nrow(Climate_Controls_DF)\n",
    "\n",
    "# #prep vars for analysis\n",
    "# #convert back to factor\n",
    "# Climate_Controls_DF[,unique_day_no:=as.factor(unique_day_no)]\n",
    "# Climate_Controls_DF[,state:=as.factor(state)]\n",
    "# Climate_Controls_DF[,country:=as.factor(country)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmin_rean2 + tmin_REAN2_l1 +      tmin_REAN2_l2 + tmin_REAN2_l3 + tmin_REAN2_l4 + tmin_REAN2_l5 +      tmin_REAN2_l6 + tmin_REAN2_l7 + tmin_norm_7_rean2 + prcp_rean2 +      prcp_norm_7_rean2 + trange_rean2 + trange_norm_7_rean2 +      cloud_rean2 + cloud_norm_7_rean2 + rhum_rean2 + rhum_norm_7_rean2 +      wind_rean2 + wind_norm_7_rean2 | userID + unique_day_no +      stateyrmn | 0 | state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-384.09  -49.17   -2.05   46.20  402.79 \n",
       "\n",
       "Coefficients:\n",
       "                      Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin_rean2          -0.2020685    0.0241590  -8.364  < 2e-16 ***\n",
       "tmin_REAN2_l1       -0.1002517    0.0149332  -6.713 1.90e-11 ***\n",
       "tmin_REAN2_l2       -0.0447558    0.0143144  -3.127  0.00177 ** \n",
       "tmin_REAN2_l3       -0.0394796    0.0170157  -2.320  0.02033 *  \n",
       "tmin_REAN2_l4       -0.0004315    0.0149286  -0.029  0.97694    \n",
       "tmin_REAN2_l5       -0.1042848    0.0195242  -5.341 9.23e-08 ***\n",
       "tmin_REAN2_l6        0.0969001    0.0254490   3.808  0.00014 ***\n",
       "tmin_REAN2_l7        0.0291404    0.0148856   1.958  0.05027 .  \n",
       "tmin_norm_7_rean2    0.0411217    0.0718435   0.572  0.56707    \n",
       "prcp_rean2           0.2795751    0.0694729   4.024 5.72e-05 ***\n",
       "prcp_norm_7_rean2   -0.0114544    0.9629116  -0.012  0.99051    \n",
       "trange_rean2        -0.2019346    0.0240371  -8.401  < 2e-16 ***\n",
       "trange_norm_7_rean2  0.0155849    0.0806121   0.193  0.84670    \n",
       "cloud_rean2          0.0147391    0.0018522   7.958 1.75e-15 ***\n",
       "cloud_norm_7_rean2  -0.0287720    0.0223629  -1.287  0.19823    \n",
       "rhum_rean2           0.0136138    0.0054506   2.498  0.01250 *  \n",
       "rhum_norm_7_rean2    0.0917349    0.0302762   3.030  0.00245 ** \n",
       "wind_rean2           0.1131505    0.0168405   6.719 1.83e-11 ***\n",
       "wind_norm_7_rean2   -0.1369710    0.1480603  -0.925  0.35491    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.94 on 7105646 degrees of freedom\n",
       "  (236416 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2758   Adjusted R-squared: 0.2688 \n",
       "Multiple R-squared(proj model): 0.0001906   Adjusted R-squared: -0.009505 \n",
       "F-statistic(full model, *iid*):39.27 on 68908 and 7105646 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 58.28 on 19 and 1256 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #distributed_lag Tmin 7 day lags\n",
    "# distributed_lag_rean2_1 <- felm(formula = sleep_duration_minutes ~ tmin_rean2 + \n",
    "#                                           tmin_REAN2_l1 + tmin_REAN2_l2 +\n",
    "#                                           tmin_REAN2_l3 + tmin_REAN2_l4 + \n",
    "#                                           tmin_REAN2_l5 + tmin_REAN2_l6 +\n",
    "#                                           tmin_REAN2_l7 + tmin_norm_7_rean2 + \n",
    "#                                           prcp_rean2 + prcp_norm_7_rean2 + \n",
    "#                                           trange_rean2 + trange_norm_7_rean2 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state,\n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(distributed_lag_rean2_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmin_rean2 + tmin_REAN2_l1 +      tmin_REAN2_l2 + tmin_REAN2_l3 + tmin_REAN2_l4 + tmin_REAN2_l5 +      tmin_REAN2_l6 + tmin_REAN2_l7 + tmin_REAN2_l8 + tmin_REAN2_l9 +      tmin_REAN2_l10 + tmin_REAN2_l11 + tmin_REAN2_l12 + tmin_REAN2_l13 +      tmin_REAN2_l14 + tmin_norm_7_rean2 + prcp_rean2 + prcp_norm_7_rean2 +      trange_rean2 + trange_norm_7_rean2 + cloud_rean2 + cloud_norm_7_rean2 +      rhum_rean2 + rhum_norm_7_rean2 + wind_rean2 + wind_norm_7_rean2 |      userID + unique_day_no + stateyrmn | 0 | state, data = Climate_Controls_DF,      na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-384.07  -49.16   -2.05   46.20  402.79 \n",
       "\n",
       "Coefficients:\n",
       "                     Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin_rean2          -0.199277     0.023498  -8.481  < 2e-16 ***\n",
       "tmin_REAN2_l1       -0.102887     0.014563  -7.065 1.61e-12 ***\n",
       "tmin_REAN2_l2       -0.043143     0.014549  -2.965 0.003022 ** \n",
       "tmin_REAN2_l3       -0.040786     0.017176  -2.375 0.017565 *  \n",
       "tmin_REAN2_l4       -0.001193     0.015128  -0.079 0.937153    \n",
       "tmin_REAN2_l5       -0.103004     0.019457  -5.294 1.20e-07 ***\n",
       "tmin_REAN2_l6        0.095001     0.025223   3.766 0.000166 ***\n",
       "tmin_REAN2_l7        0.034509     0.015748   2.191 0.028426 *  \n",
       "tmin_REAN2_l8        0.009580     0.017690   0.542 0.588112    \n",
       "tmin_REAN2_l9       -0.026438     0.018349  -1.441 0.149635    \n",
       "tmin_REAN2_l10      -0.008348     0.021971  -0.380 0.703984    \n",
       "tmin_REAN2_l11      -0.013411     0.015598  -0.860 0.389888    \n",
       "tmin_REAN2_l12       0.006417     0.017364   0.370 0.711720    \n",
       "tmin_REAN2_l13       0.067789     0.019768   3.429 0.000605 ***\n",
       "tmin_REAN2_l14      -0.023573     0.019929  -1.183 0.236872    \n",
       "tmin_norm_7_rean2    0.031088     0.075093   0.414 0.678878    \n",
       "prcp_rean2           0.272464     0.068427   3.982 6.84e-05 ***\n",
       "prcp_norm_7_rean2   -0.011487     0.964902  -0.012 0.990502    \n",
       "trange_rean2        -0.200036     0.023746  -8.424  < 2e-16 ***\n",
       "trange_norm_7_rean2  0.009075     0.080755   0.112 0.910520    \n",
       "cloud_rean2          0.014519     0.001872   7.756 8.75e-15 ***\n",
       "cloud_norm_7_rean2  -0.027601     0.022202  -1.243 0.213801    \n",
       "rhum_rean2           0.013992     0.005407   2.588 0.009660 ** \n",
       "rhum_norm_7_rean2    0.090517     0.030202   2.997 0.002726 ** \n",
       "wind_rean2           0.114926     0.017179   6.690 2.23e-11 ***\n",
       "wind_norm_7_rean2   -0.138959     0.153266  -0.907 0.364593    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.94 on 7105505 degrees of freedom\n",
       "  (236555 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2758   Adjusted R-squared: 0.2688 \n",
       "Multiple R-squared(proj model): 0.0001969   Adjusted R-squared: -0.009499 \n",
       "F-statistic(full model, *iid*):39.27 on 68910 and 7105505 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 45.87 on 26 and 1256 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #distributed_lag model - 14 day tmin lags\n",
    "# distributed_lag_rean2_2 <- felm(formula = sleep_duration_minutes ~ tmin_rean2 + \n",
    "#                                           tmin_REAN2_l1 + tmin_REAN2_l2 +\n",
    "#                                           tmin_REAN2_l3 + tmin_REAN2_l4 + \n",
    "#                                           tmin_REAN2_l5 + tmin_REAN2_l6 +\n",
    "#                                           tmin_REAN2_l7 + tmin_REAN2_l8 +\n",
    "#                                           tmin_REAN2_l9 + tmin_REAN2_l10 +\n",
    "#                                           tmin_REAN2_l11 + tmin_REAN2_l12 + \n",
    "#                                           tmin_REAN2_l13 + tmin_REAN2_l14 + \n",
    "#                                           tmin_norm_7_rean2 + \n",
    "#                                           prcp_rean2 + prcp_norm_7_rean2 + \n",
    "#                                           trange_rean2 + trange_norm_7_rean2 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state,\n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(distributed_lag_rean2_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmin_rean2 + tmin_REAN2_l1 +      tmin_REAN2_l2 + tmin_REAN2_l3 + tmin_REAN2_l4 + tmin_REAN2_l5 +      tmin_REAN2_l6 + tmin_REAN2_l7 + tmin_norm_7_rean2 + prcp_rean2 +      prcp_REAN2_l1 + prcp_REAN2_l2 + prcp_REAN2_l3 + prcp_REAN2_l4 +      prcp_REAN2_l5 + prcp_REAN2_l6 + prcp_REAN2_l7 + prcp_norm_7_rean2 +      trange_rean2 + trange_REAN2_l1 + trange_REAN2_l2 + trange_REAN2_l3 +      trange_REAN2_l4 + trange_REAN2_l5 + trange_REAN2_l6 + trange_REAN2_l7 +      trange_norm_7_rean2 + cloud_rean2 + cloud_REAN2_l1 + cloud_REAN2_l2 +      cloud_REAN2_l3 + cloud_REAN2_l4 + cloud_REAN2_l5 + cloud_REAN2_l6 +      cloud_REAN2_l7 + cloud_norm_7_rean2 + rhum_REAN2 + rhum_REAN2_l1 +      rhum_REAN2_l2 + rhum_REAN2_l3 + rhum_REAN2_l4 + rhum_REAN2_l5 +      rhum_REAN2_l6 + rhum_REAN2_l7 + rhum_norm_7_rean2 + wind_rean2 +      wind_REAN2_l1 + wind_REAN2_l2 + wind_REAN2_l3 + wind_REAN2_l4 +      wind_REAN2_l5 + wind_REAN2_l6 + wind_REAN2_l7 + wind_norm_7_rean2 |      userID + unique_day_no + stateyrmn | 0 | state, data = Climate_Controls_DF,      na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-384.92  -49.16   -2.04   46.20  402.33 \n",
       "\n",
       "Coefficients:\n",
       "                      Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin_rean2          -0.1926488    0.0206032  -9.350  < 2e-16 ***\n",
       "tmin_REAN2_l1       -0.1312279    0.0255997  -5.126 2.96e-07 ***\n",
       "tmin_REAN2_l2       -0.0337106    0.0224395  -1.502 0.133023    \n",
       "tmin_REAN2_l3       -0.0587140    0.0205575  -2.856 0.004289 ** \n",
       "tmin_REAN2_l4        0.0298181    0.0223953   1.331 0.183044    \n",
       "tmin_REAN2_l5       -0.1551178    0.0275059  -5.639 1.71e-08 ***\n",
       "tmin_REAN2_l6        0.1360178    0.0361515   3.762 0.000168 ***\n",
       "tmin_REAN2_l7        0.0517419    0.0192907   2.682 0.007314 ** \n",
       "tmin_norm_7_rean2    0.0353881    0.0734879   0.482 0.630126    \n",
       "prcp_rean2           0.2285072    0.0702279   3.254 0.001139 ** \n",
       "prcp_REAN2_l1       -0.0121692    0.0702561  -0.173 0.862485    \n",
       "prcp_REAN2_l2        0.0072474    0.0683419   0.106 0.915546    \n",
       "prcp_REAN2_l3       -0.1798875    0.0680222  -2.645 0.008180 ** \n",
       "prcp_REAN2_l4        0.0901296    0.0745206   1.209 0.226487    \n",
       "prcp_REAN2_l5        0.0352294    0.0716666   0.492 0.623021    \n",
       "prcp_REAN2_l6       -0.0857368    0.0836099  -1.025 0.305156    \n",
       "prcp_REAN2_l7        0.0215942    0.0848297   0.255 0.799063    \n",
       "prcp_norm_7_rean2    0.1991581    0.9956897   0.200 0.841465    \n",
       "trange_rean2        -0.1744705    0.0211100  -8.265  < 2e-16 ***\n",
       "trange_REAN2_l1     -0.0717636    0.0346140  -2.073 0.038149 *  \n",
       "trange_REAN2_l2     -0.0123643    0.0330052  -0.375 0.707945    \n",
       "trange_REAN2_l3     -0.0542531    0.0271667  -1.997 0.045821 *  \n",
       "trange_REAN2_l4     -0.0162768    0.0256045  -0.636 0.524972    \n",
       "trange_REAN2_l5     -0.1255398    0.0262074  -4.790 1.67e-06 ***\n",
       "trange_REAN2_l6      0.1351239    0.0426602   3.167 0.001538 ** \n",
       "trange_REAN2_l7      0.0951043    0.0292941   3.247 0.001168 ** \n",
       "trange_norm_7_rean2 -0.0010345    0.0810363  -0.013 0.989815    \n",
       "cloud_rean2          0.0145494    0.0017278   8.421  < 2e-16 ***\n",
       "cloud_REAN2_l1      -0.0049272    0.0028616  -1.722 0.085099 .  \n",
       "cloud_REAN2_l2      -0.0018354    0.0022259  -0.825 0.409612    \n",
       "cloud_REAN2_l3      -0.0052136    0.0027118  -1.923 0.054535 .  \n",
       "cloud_REAN2_l4      -0.0084273    0.0019376  -4.349 1.36e-05 ***\n",
       "cloud_REAN2_l5      -0.0090291    0.0024141  -3.740 0.000184 ***\n",
       "cloud_REAN2_l6      -0.0001969    0.0027291  -0.072 0.942484    \n",
       "cloud_REAN2_l7       0.0088255    0.0020999   4.203 2.64e-05 ***\n",
       "cloud_norm_7_rean2  -0.0226032    0.0217571  -1.039 0.298857    \n",
       "rhum_REAN2           0.0301232    0.0052918   5.692 1.25e-08 ***\n",
       "rhum_REAN2_l1       -0.0190707    0.0090340  -2.111 0.034773 *  \n",
       "rhum_REAN2_l2        0.0116195    0.0049480   2.348 0.018858 *  \n",
       "rhum_REAN2_l3       -0.0081902    0.0074899  -1.093 0.274176    \n",
       "rhum_REAN2_l4        0.0096020    0.0062700   1.531 0.125664    \n",
       "rhum_REAN2_l5       -0.0063246    0.0064232  -0.985 0.324795    \n",
       "rhum_REAN2_l6        0.0274895    0.0077133   3.564 0.000365 ***\n",
       "rhum_REAN2_l7       -0.0206872    0.0048116  -4.299 1.71e-05 ***\n",
       "rhum_norm_7_rean2    0.0915959    0.0310652   2.949 0.003193 ** \n",
       "wind_rean2           0.1054371    0.0153871   6.852 7.27e-12 ***\n",
       "wind_REAN2_l1        0.0638861    0.0206148   3.099 0.001941 ** \n",
       "wind_REAN2_l2       -0.0242128    0.0188171  -1.287 0.198185    \n",
       "wind_REAN2_l3        0.0784234    0.0164455   4.769 1.85e-06 ***\n",
       "wind_REAN2_l4       -0.0005702    0.0223848  -0.025 0.979677    \n",
       "wind_REAN2_l5       -0.0382616    0.0206024  -1.857 0.063291 .  \n",
       "wind_REAN2_l6        0.0540906    0.0180929   2.990 0.002793 ** \n",
       "wind_REAN2_l7       -0.0765426    0.0183225  -4.178 2.95e-05 ***\n",
       "wind_norm_7_rean2   -0.0943615    0.1374665  -0.686 0.492441    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.93 on 7091203 degrees of freedom\n",
       "  (250879 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2759   Adjusted R-squared: 0.2689 \n",
       "Multiple R-squared(proj model): 0.0002596   Adjusted R-squared: -0.009452 \n",
       "F-statistic(full model, *iid*):39.23 on 68888 and 7091203 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 40.76 on 54 and 1256 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #distributed_lag model 7 day lags for all weather vars\n",
    "# distributed_lag_rean2_3 <- felm(formula = sleep_duration_minutes ~ tmin_rean2 + \n",
    "#                                           tmin_REAN2_l1 + tmin_REAN2_l2 +\n",
    "#                                           tmin_REAN2_l3 + tmin_REAN2_l4 + \n",
    "#                                           tmin_REAN2_l5 + tmin_REAN2_l6 +\n",
    "#                                           tmin_REAN2_l7 + tmin_norm_7_rean2 + \n",
    "#                                           prcp_rean2 + \n",
    "#                                           prcp_REAN2_l1 + prcp_REAN2_l2 +\n",
    "#                                           prcp_REAN2_l3 + prcp_REAN2_l4 + \n",
    "#                                           prcp_REAN2_l5 + prcp_REAN2_l6 +\n",
    "#                                           prcp_REAN2_l7 + prcp_norm_7_rean2 + \n",
    "#                                           trange_rean2 +\n",
    "#                                           trange_REAN2_l1 + trange_REAN2_l2 +\n",
    "#                                           trange_REAN2_l3 + trange_REAN2_l4 + \n",
    "#                                           trange_REAN2_l5 + trange_REAN2_l6 +\n",
    "#                                           trange_REAN2_l7 + trange_norm_7_rean2 + \n",
    "#                                           cloud_rean2 + \n",
    "#                                           cloud_REAN2_l1 + cloud_REAN2_l2 +\n",
    "#                                           cloud_REAN2_l3 + cloud_REAN2_l4 + \n",
    "#                                           cloud_REAN2_l5 + cloud_REAN2_l6 +\n",
    "#                                           cloud_REAN2_l7 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_REAN2 + \n",
    "#                                           rhum_REAN2_l1 + rhum_REAN2_l2 +\n",
    "#                                           rhum_REAN2_l3 + rhum_REAN2_l4 + \n",
    "#                                           rhum_REAN2_l5 + rhum_REAN2_l6 +\n",
    "#                                           rhum_REAN2_l7 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + \n",
    "#                                           wind_REAN2_l1 + wind_REAN2_l2 +\n",
    "#                                           wind_REAN2_l3 + wind_REAN2_l4 + \n",
    "#                                           wind_REAN2_l5 + wind_REAN2_l6 +\n",
    "#                                           wind_REAN2_l7 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state,\n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(distributed_lag_rean2_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{Table 37: Distributed Lag Models} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lccc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{3}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-4} \n",
      " & Tmin 7lags & Tmin 14lags & All weather 7lags \\\\ \n",
      "\\\\[-1.8ex] & (1) & (2) & (3)\\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " tmin\\_rean2 & $-$0.202$^{***}$ & $-$0.199$^{***}$ & $-$0.193$^{***}$ \\\\ \n",
      "  & (0.024) & (0.023) & (0.021) \\\\ \n",
      "  tmin\\_REAN2\\_l1 & $-$0.100$^{***}$ & $-$0.103$^{***}$ & $-$0.131$^{***}$ \\\\ \n",
      "  & (0.015) & (0.015) & (0.026) \\\\ \n",
      "  tmin\\_REAN2\\_l2 & $-$0.045$^{***}$ & $-$0.043$^{***}$ & $-$0.034 \\\\ \n",
      "  & (0.014) & (0.015) & (0.022) \\\\ \n",
      "  tmin\\_REAN2\\_l3 & $-$0.039$^{**}$ & $-$0.041$^{**}$ & $-$0.059$^{***}$ \\\\ \n",
      "  & (0.017) & (0.017) & (0.021) \\\\ \n",
      "  tmin\\_REAN2\\_l4 & $-$0.0004 & $-$0.001 & 0.030 \\\\ \n",
      "  & (0.015) & (0.015) & (0.022) \\\\ \n",
      "  tmin\\_REAN2\\_l5 & $-$0.104$^{***}$ & $-$0.103$^{***}$ & $-$0.155$^{***}$ \\\\ \n",
      "  & (0.020) & (0.019) & (0.028) \\\\ \n",
      "  tmin\\_REAN2\\_l6 & 0.097$^{***}$ & 0.095$^{***}$ & 0.136$^{***}$ \\\\ \n",
      "  & (0.025) & (0.025) & (0.036) \\\\ \n",
      "  tmin\\_REAN2\\_l7 & 0.029$^{*}$ & 0.035$^{**}$ & 0.052$^{***}$ \\\\ \n",
      "  & (0.015) & (0.016) & (0.019) \\\\ \n",
      "  tmin\\_REAN2\\_l8 &  & 0.010 &  \\\\ \n",
      "  &  & (0.018) &  \\\\ \n",
      "  tmin\\_REAN2\\_l9 &  & $-$0.026 &  \\\\ \n",
      "  &  & (0.018) &  \\\\ \n",
      "  tmin\\_REAN2\\_l10 &  & $-$0.008 &  \\\\ \n",
      "  &  & (0.022) &  \\\\ \n",
      "  tmin\\_REAN2\\_l11 &  & $-$0.013 &  \\\\ \n",
      "  &  & (0.016) &  \\\\ \n",
      "  tmin\\_REAN2\\_l12 &  & 0.006 &  \\\\ \n",
      "  &  & (0.017) &  \\\\ \n",
      "  tmin\\_REAN2\\_l13 &  & 0.068$^{***}$ &  \\\\ \n",
      "  &  & (0.020) &  \\\\ \n",
      "  tmin\\_REAN2\\_l14 &  & $-$0.024 &  \\\\ \n",
      "  &  & (0.020) &  \\\\ \n",
      "  tmin\\_norm\\_7\\_rean2 & 0.041 & 0.031 & 0.035 \\\\ \n",
      "  & (0.072) & (0.075) & (0.073) \\\\ \n",
      "  prcp\\_rean2 & 0.280$^{***}$ & 0.272$^{***}$ & 0.229$^{***}$ \\\\ \n",
      "  & (0.069) & (0.068) & (0.070) \\\\ \n",
      "  prcp\\_REAN2\\_l1 &  &  & $-$0.012 \\\\ \n",
      "  &  &  & (0.070) \\\\ \n",
      "  prcp\\_REAN2\\_l2 &  &  & 0.007 \\\\ \n",
      "  &  &  & (0.068) \\\\ \n",
      "  prcp\\_REAN2\\_l3 &  &  & $-$0.180$^{***}$ \\\\ \n",
      "  &  &  & (0.068) \\\\ \n",
      "  prcp\\_REAN2\\_l4 &  &  & 0.090 \\\\ \n",
      "  &  &  & (0.075) \\\\ \n",
      "  prcp\\_REAN2\\_l5 &  &  & 0.035 \\\\ \n",
      "  &  &  & (0.072) \\\\ \n",
      "  prcp\\_REAN2\\_l6 &  &  & $-$0.086 \\\\ \n",
      "  &  &  & (0.084) \\\\ \n",
      "  prcp\\_REAN2\\_l7 &  &  & 0.022 \\\\ \n",
      "  &  &  & (0.085) \\\\ \n",
      "  prcp\\_norm\\_7\\_rean2 & $-$0.011 & $-$0.011 & 0.199 \\\\ \n",
      "  & (0.963) & (0.965) & (0.996) \\\\ \n",
      "  trange\\_rean2 & $-$0.202$^{***}$ & $-$0.200$^{***}$ & $-$0.174$^{***}$ \\\\ \n",
      "  & (0.024) & (0.024) & (0.021) \\\\ \n",
      "  trange\\_REAN2\\_l1 &  &  & $-$0.072$^{**}$ \\\\ \n",
      "  &  &  & (0.035) \\\\ \n",
      "  trange\\_REAN2\\_l2 &  &  & $-$0.012 \\\\ \n",
      "  &  &  & (0.033) \\\\ \n",
      "  trange\\_REAN2\\_l3 &  &  & $-$0.054$^{**}$ \\\\ \n",
      "  &  &  & (0.027) \\\\ \n",
      "  trange\\_REAN2\\_l4 &  &  & $-$0.016 \\\\ \n",
      "  &  &  & (0.026) \\\\ \n",
      "  trange\\_REAN2\\_l5 &  &  & $-$0.126$^{***}$ \\\\ \n",
      "  &  &  & (0.026) \\\\ \n",
      "  trange\\_REAN2\\_l6 &  &  & 0.135$^{***}$ \\\\ \n",
      "  &  &  & (0.043) \\\\ \n",
      "  trange\\_REAN2\\_l7 &  &  & 0.095$^{***}$ \\\\ \n",
      "  &  &  & (0.029) \\\\ \n",
      "  trange\\_norm\\_7\\_rean2 & 0.016 & 0.009 & $-$0.001 \\\\ \n",
      "  & (0.081) & (0.081) & (0.081) \\\\ \n",
      "  cloud\\_rean2 & 0.015$^{***}$ & 0.015$^{***}$ & 0.015$^{***}$ \\\\ \n",
      "  & (0.002) & (0.002) & (0.002) \\\\ \n",
      "  cloud\\_REAN2\\_l1 &  &  & $-$0.005$^{*}$ \\\\ \n",
      "  &  &  & (0.003) \\\\ \n",
      "  cloud\\_REAN2\\_l2 &  &  & $-$0.002 \\\\ \n",
      "  &  &  & (0.002) \\\\ \n",
      "  cloud\\_REAN2\\_l3 &  &  & $-$0.005$^{*}$ \\\\ \n",
      "  &  &  & (0.003) \\\\ \n",
      "  cloud\\_REAN2\\_l4 &  &  & $-$0.008$^{***}$ \\\\ \n",
      "  &  &  & (0.002) \\\\ \n",
      "  cloud\\_REAN2\\_l5 &  &  & $-$0.009$^{***}$ \\\\ \n",
      "  &  &  & (0.002) \\\\ \n",
      "  cloud\\_REAN2\\_l6 &  &  & $-$0.0002 \\\\ \n",
      "  &  &  & (0.003) \\\\ \n",
      "  cloud\\_REAN2\\_l7 &  &  & 0.009$^{***}$ \\\\ \n",
      "  &  &  & (0.002) \\\\ \n",
      "  cloud\\_norm\\_7\\_rean2 & $-$0.029 & $-$0.028 & $-$0.023 \\\\ \n",
      "  & (0.022) & (0.022) & (0.022) \\\\ \n",
      "  rhum\\_rean2 & 0.014$^{**}$ & 0.014$^{***}$ &  \\\\ \n",
      "  & (0.005) & (0.005) &  \\\\ \n",
      "  rhum\\_REAN2 &  &  & 0.030$^{***}$ \\\\ \n",
      "  &  &  & (0.005) \\\\ \n",
      "  rhum\\_REAN2\\_l1 &  &  & $-$0.019$^{**}$ \\\\ \n",
      "  &  &  & (0.009) \\\\ \n",
      "  rhum\\_REAN2\\_l2 &  &  & 0.012$^{**}$ \\\\ \n",
      "  &  &  & (0.005) \\\\ \n",
      "  rhum\\_REAN2\\_l3 &  &  & $-$0.008 \\\\ \n",
      "  &  &  & (0.007) \\\\ \n",
      "  rhum\\_REAN2\\_l4 &  &  & 0.010 \\\\ \n",
      "  &  &  & (0.006) \\\\ \n",
      "  rhum\\_REAN2\\_l5 &  &  & $-$0.006 \\\\ \n",
      "  &  &  & (0.006) \\\\ \n",
      "  rhum\\_REAN2\\_l6 &  &  & 0.027$^{***}$ \\\\ \n",
      "  &  &  & (0.008) \\\\ \n",
      "  rhum\\_REAN2\\_l7 &  &  & $-$0.021$^{***}$ \\\\ \n",
      "  &  &  & (0.005) \\\\ \n",
      "  rhum\\_norm\\_7\\_rean2 & 0.092$^{***}$ & 0.091$^{***}$ & 0.092$^{***}$ \\\\ \n",
      "  & (0.030) & (0.030) & (0.031) \\\\ \n",
      "  wind\\_rean2 & 0.113$^{***}$ & 0.115$^{***}$ & 0.105$^{***}$ \\\\ \n",
      "  & (0.017) & (0.017) & (0.015) \\\\ \n",
      "  wind\\_REAN2\\_l1 &  &  & 0.064$^{***}$ \\\\ \n",
      "  &  &  & (0.021) \\\\ \n",
      "  wind\\_REAN2\\_l2 &  &  & $-$0.024 \\\\ \n",
      "  &  &  & (0.019) \\\\ \n",
      "  wind\\_REAN2\\_l3 &  &  & 0.078$^{***}$ \\\\ \n",
      "  &  &  & (0.016) \\\\ \n",
      "  wind\\_REAN2\\_l4 &  &  & $-$0.001 \\\\ \n",
      "  &  &  & (0.022) \\\\ \n",
      "  wind\\_REAN2\\_l5 &  &  & $-$0.038$^{*}$ \\\\ \n",
      "  &  &  & (0.021) \\\\ \n",
      "  wind\\_REAN2\\_l6 &  &  & 0.054$^{***}$ \\\\ \n",
      "  &  &  & (0.018) \\\\ \n",
      "  wind\\_REAN2\\_l7 &  &  & $-$0.077$^{***}$ \\\\ \n",
      "  &  &  & (0.018) \\\\ \n",
      "  wind\\_norm\\_7\\_rean2 & $-$0.137 & $-$0.139 & $-$0.094 \\\\ \n",
      "  & (0.148) & (0.153) & (0.137) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes & Yes & Yes \\\\ \n",
      "Date FE & Yes & Yes & Yes \\\\ \n",
      "Admin_2:Month FE & Yes & Yes & Yes \\\\ \n",
      "Observations & 7,174,555 & 7,174,416 & 7,160,092 \\\\ \n",
      "R$^{2}$ & 0.276 & 0.276 & 0.276 \\\\ \n",
      "Adjusted R$^{2}$ & 0.269 & 0.269 & 0.269 \\\\ \n",
      "Residual Std. Error & 77.936 & 77.936 & 77.932 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{3}{r}{Standard errors are in parentheses and are clustered on the 1st-level admin division} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# invisible(stargazer(distributed_lag_rean2_1, distributed_lag_rean2_2, distributed_lag_rean2_3,\n",
    "#           type='latex',\n",
    "#           title = \"Distributed Lag Models\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Tmin 7lags','Tmin 14lags','All weather 7lags'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\",\"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Admin_2:Month FE\", \"Yes\", \"Yes\",\"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the 1st-level admin division'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Alternative Unit of Analysis (Admin Day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# linear_t_admin1 <- felm(formula = sleep_duration_minutes ~ tmin + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | state + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = adm1, na.action = na.omit) \n",
    "# summary(linear_t_admin1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{Alternative unit of analysis: admin1_day (linear model)} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{1}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-2} \n",
      " & Linear TMIN \\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " tmin & $-$0.343$^{***}$ \\\\ \n",
      "  & (0.035) \\\\ \n",
      "  tmin\\_norm\\_w7 & $-$0.688$^{***}$ \\\\ \n",
      "  & (0.143) \\\\ \n",
      "  prcp & 0.011 \\\\ \n",
      "  & (0.165) \\\\ \n",
      "  prcp\\_norm\\_w7 & $-$2.161 \\\\ \n",
      "  & (3.129) \\\\ \n",
      "  trange & $-$0.200$^{***}$ \\\\ \n",
      "  & (0.030) \\\\ \n",
      "  trange\\_norm\\_w7 & 0.198 \\\\ \n",
      "  & (0.307) \\\\ \n",
      "  cloud\\_rean2 & 0.011$^{***}$ \\\\ \n",
      "  & (0.003) \\\\ \n",
      "  cloud\\_norm\\_7\\_rean2 & 0.027 \\\\ \n",
      "  & (0.046) \\\\ \n",
      "  rhum\\_rean2 & 0.028$^{**}$ \\\\ \n",
      "  & (0.011) \\\\ \n",
      "  rhum\\_norm\\_7\\_rean2 & 0.037 \\\\ \n",
      "  & (0.037) \\\\ \n",
      "  wind\\_rean2 & 0.125$^{***}$ \\\\ \n",
      "  & (0.029) \\\\ \n",
      "  wind\\_norm\\_7\\_rean2 & 1.272$^{***}$ \\\\ \n",
      "  & (0.246) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes \\\\ \n",
      "Date FE & Yes \\\\ \n",
      "State:Month FE & Yes \\\\ \n",
      "Observations & 86,098 \\\\ \n",
      "R$^{2}$ & 0.697 \\\\ \n",
      "Adjusted R$^{2}$ & 0.666 \\\\ \n",
      "Residual Std. Error & 18.233 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{1}{r}{Standard errors are in parentheses and are clustered on the state admin level} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# invisible(stargazer(linear_t_admin1,\n",
    "#           type='latex',\n",
    "#           title = \"Alternative unit of analysis: admin1_day (linear model)\",\n",
    "#           out='linear_tmin.tex',\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Linear TMIN'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the state admin level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Alternative Unit of Analysis: Admin1_Day (Binned Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ CUTTMIN + tmin_norm_w7 +      CUTPRCP + prcp_norm_w7 + CUTTRANGE + trange_norm_w7 + CUTCLOUD_rean2 +      cloud_norm_7_rean2 + CUTRHUM_rean2 + rhum_norm_7_rean2 +      CUTWIND_rean2 + wind_norm_7_rean2 | state + unique_day_no +      stateyrmn | 0 | state, data = adm1, na.action = na.omit,      weights = adm$user_count) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-99.070  -9.969  -0.237   9.772 105.587 \n",
       "\n",
       "Coefficients:\n",
       "                       Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "CUTTMIN(-Inf,-15]       3.89430      1.25062   3.114  0.00185 ** \n",
       "CUTTMIN(-15,-10]        1.24991      1.04842   1.192  0.23319    \n",
       "CUTTMIN(-10,-5]         1.68369      0.61105   2.755  0.00586 ** \n",
       "CUTTMIN(-5,0]           0.59575      0.40309   1.478  0.13942    \n",
       "CUTTMIN(0,5]            0.84304      0.27732   3.040  0.00237 ** \n",
       "CUTTMIN(10,15]         -2.22112      0.30210  -7.352 1.97e-13 ***\n",
       "CUTTMIN(15,20]         -5.19694      0.49284 -10.545  < 2e-16 ***\n",
       "CUTTMIN(20,25]         -8.00035      0.67176 -11.910  < 2e-16 ***\n",
       "CUTTMIN(25, Inf]       -9.60823      1.41311  -6.799 1.06e-11 ***\n",
       "tmin_norm_w7           -0.64032      0.14124  -4.533 5.81e-06 ***\n",
       "CUTPRCP(0,1]            0.31234      0.21498   1.453  0.14627    \n",
       "CUTPRCP(1,2]            0.32820      0.41158   0.797  0.42522    \n",
       "CUTPRCP(2,3]            1.17217      0.60967   1.923  0.05453 .  \n",
       "CUTPRCP(3, Inf]         0.33490      1.16760   0.287  0.77425    \n",
       "prcp_norm_w7           -2.46301      3.21136  -0.767  0.44310    \n",
       "CUTTRANGE(-Inf,0]       0.62515      1.39443   0.448  0.65392    \n",
       "CUTTRANGE(0,5]          0.27155      0.22214   1.222  0.22155    \n",
       "CUTTRANGE(10,15]       -0.90852      0.17660  -5.145 2.69e-07 ***\n",
       "CUTTRANGE(15,20]       -1.78571      0.38580  -4.629 3.69e-06 ***\n",
       "CUTTRANGE(20, Inf]     -3.83335      1.31613  -2.913  0.00359 ** \n",
       "trange_norm_w7          0.11286      0.32594   0.346  0.72914    \n",
       "CUTCLOUD_rean2(0,20]    1.40218      0.96179   1.458  0.14488    \n",
       "CUTCLOUD_rean2(20,40]   1.67689      0.98052   1.710  0.08723 .  \n",
       "CUTCLOUD_rean2(40,60]   1.42177      0.98166   1.448  0.14753    \n",
       "CUTCLOUD_rean2(60,80]   1.89905      0.95454   1.990  0.04665 *  \n",
       "CUTCLOUD_rean2(80,100]  2.49901      0.97108   2.573  0.01007 *  \n",
       "cloud_norm_7_rean2      0.02798      0.03910   0.716  0.47418    \n",
       "CUTRHUM_rean2(0,20]     3.23011      4.14760   0.779  0.43611    \n",
       "CUTRHUM_rean2(20,40]    0.19325      1.46948   0.132  0.89537    \n",
       "CUTRHUM_rean2(40,60]   -0.15248      0.35846  -0.425  0.67057    \n",
       "CUTRHUM_rean2(80,100]   0.20368      0.20243   1.006  0.31434    \n",
       "rhum_norm_7_rean2       0.06044      0.04748   1.273  0.20311    \n",
       "CUTWIND_rean2(5,10]     0.22461      0.16510   1.360  0.17370    \n",
       "CUTWIND_rean2(10,Inf]   0.87645      0.34029   2.576  0.01001 *  \n",
       "wind_norm_7_rean2       1.21639      0.26561   4.580 4.66e-06 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 18.23 on 77974 degrees of freedom\n",
       "  (25775 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.6977   Adjusted R-squared: 0.6662 \n",
       "Multiple R-squared(proj model): 0.01317   Adjusted R-squared: -0.08963 \n",
       "F-statistic(full model, *iid*):22.15 on 8123 and 77974 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 80.14 on 35 and 443 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Robustness: Admin1 level aggregation\n",
    "# flex_admin1 <- felm(formula = sleep_duration_minutes ~ CUTTMIN + tmin_norm_w7 + \n",
    "#                                           CUTPRCP + prcp_norm_w7 + \n",
    "#                                           CUTTRANGE + trange_norm_w7 + \n",
    "#                                           CUTCLOUD_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           CUTRHUM_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           CUTWIND_rean2 + wind_norm_7_rean2 \n",
    "#                 | state + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = adm1, na.action = na.omit, weights = adm$user_count) \n",
    "# summary(flex_admin1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{Alternative unit of analysis: admin1_day (binned model)} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{1}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-2} \n",
      " & Flexible TMIN GHCND \\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " CUTTMIN(-Inf,-15] & 3.894$^{***}$ \\\\ \n",
      "  & (1.251) \\\\ \n",
      "  CUTTMIN(-15,-10] & 1.250 \\\\ \n",
      "  & (1.048) \\\\ \n",
      "  CUTTMIN(-10,-5] & 1.684$^{***}$ \\\\ \n",
      "  & (0.611) \\\\ \n",
      "  CUTTMIN(-5,0] & 0.596 \\\\ \n",
      "  & (0.403) \\\\ \n",
      "  CUTTMIN(0,5] & 0.843$^{***}$ \\\\ \n",
      "  & (0.277) \\\\ \n",
      "  CUTTMIN(10,15] & $-$2.221$^{***}$ \\\\ \n",
      "  & (0.302) \\\\ \n",
      "  CUTTMIN(15,20] & $-$5.197$^{***}$ \\\\ \n",
      "  & (0.493) \\\\ \n",
      "  CUTTMIN(20,25] & $-$8.000$^{***}$ \\\\ \n",
      "  & (0.672) \\\\ \n",
      "  CUTTMIN(25, Inf] & $-$9.608$^{***}$ \\\\ \n",
      "  & (1.413) \\\\ \n",
      "  tmin\\_norm\\_w7 & $-$0.640$^{***}$ \\\\ \n",
      "  & (0.141) \\\\ \n",
      "  CUTPRCP(0,1] & 0.312 \\\\ \n",
      "  & (0.215) \\\\ \n",
      "  CUTPRCP(1,2] & 0.328 \\\\ \n",
      "  & (0.412) \\\\ \n",
      "  CUTPRCP(2,3] & 1.172$^{*}$ \\\\ \n",
      "  & (0.610) \\\\ \n",
      "  CUTPRCP(3, Inf] & 0.335 \\\\ \n",
      "  & (1.168) \\\\ \n",
      "  prcp\\_norm\\_w7 & $-$2.463 \\\\ \n",
      "  & (3.211) \\\\ \n",
      "  CUTTRANGE(-Inf,0] & 0.625 \\\\ \n",
      "  & (1.394) \\\\ \n",
      "  CUTTRANGE(0,5] & 0.272 \\\\ \n",
      "  & (0.222) \\\\ \n",
      "  CUTTRANGE(10,15] & $-$0.909$^{***}$ \\\\ \n",
      "  & (0.177) \\\\ \n",
      "  CUTTRANGE(15,20] & $-$1.786$^{***}$ \\\\ \n",
      "  & (0.386) \\\\ \n",
      "  CUTTRANGE(20, Inf] & $-$3.833$^{***}$ \\\\ \n",
      "  & (1.316) \\\\ \n",
      "  trange\\_norm\\_w7 & 0.113 \\\\ \n",
      "  & (0.326) \\\\ \n",
      "  CUTCLOUD\\_rean2(0,20] & 1.402 \\\\ \n",
      "  & (0.962) \\\\ \n",
      "  CUTCLOUD\\_rean2(20,40] & 1.677$^{*}$ \\\\ \n",
      "  & (0.981) \\\\ \n",
      "  CUTCLOUD\\_rean2(40,60] & 1.422 \\\\ \n",
      "  & (0.982) \\\\ \n",
      "  CUTCLOUD\\_rean2(60,80] & 1.899$^{**}$ \\\\ \n",
      "  & (0.955) \\\\ \n",
      "  CUTCLOUD\\_rean2(80,100] & 2.499$^{**}$ \\\\ \n",
      "  & (0.971) \\\\ \n",
      "  cloud\\_norm\\_7\\_rean2 & 0.028 \\\\ \n",
      "  & (0.039) \\\\ \n",
      "  CUTRHUM\\_rean2(0,20] & 3.230 \\\\ \n",
      "  & (4.148) \\\\ \n",
      "  CUTRHUM\\_rean2(20,40] & 0.193 \\\\ \n",
      "  & (1.469) \\\\ \n",
      "  CUTRHUM\\_rean2(40,60] & $-$0.152 \\\\ \n",
      "  & (0.358) \\\\ \n",
      "  CUTRHUM\\_rean2(80,100] & 0.204 \\\\ \n",
      "  & (0.202) \\\\ \n",
      "  rhum\\_norm\\_7\\_rean2 & 0.060 \\\\ \n",
      "  & (0.047) \\\\ \n",
      "  CUTWIND\\_rean2(5,10] & 0.225 \\\\ \n",
      "  & (0.165) \\\\ \n",
      "  CUTWIND\\_rean2(10,Inf] & 0.876$^{**}$ \\\\ \n",
      "  & (0.340) \\\\ \n",
      "  wind\\_norm\\_7\\_rean2 & 1.216$^{***}$ \\\\ \n",
      "  & (0.266) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes \\\\ \n",
      "Date FE & Yes \\\\ \n",
      "Admin_2:Month FE & Yes \\\\ \n",
      "Observations & 86,098 \\\\ \n",
      "R$^{2}$ & 0.698 \\\\ \n",
      "Adjusted R$^{2}$ & 0.666 \\\\ \n",
      "Residual Std. Error & 18.228 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{1}{r}{Standard errors are in parentheses and are clustered on the Admin 1 level} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# invisible(stargazer(flex_admin1,\n",
    "#           type='latex',\n",
    "#           title = \"Alternative unit of analysis: admin1_day (binned model)\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Flexible TMIN GHCND'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\",\"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Admin_2:Month FE\", \"Yes\", \"Yes\",\"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the Admin 1 level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Alternative Temperature Vars: Minimum vs. Max Daily Temperature (no diurnal temp range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmin + tmin_norm_w7 +      prcp + prcp_norm_w7 + cloud_rean2 + cloud_norm_7_rean2 +      rhum_rean2 + rhum_norm_7_rean2 + wind_rean2 + wind_norm_7_rean2 |      userID + unique_day_no + stateyrmn | 0 | state, data = Climate_Controls_DF,      na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-382.73  -49.07   -2.24   45.85  407.55 \n",
       "\n",
       "Coefficients:\n",
       "                    Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin               -0.226101     0.026498  -8.533  < 2e-16 ***\n",
       "tmin_norm_w7       -0.222226     0.078808  -2.820 0.004805 ** \n",
       "prcp                0.628188     0.114473   5.488 4.07e-08 ***\n",
       "prcp_norm_w7        1.569605     1.062305   1.478 0.139529    \n",
       "cloud_rean2         0.012361     0.002183   5.662 1.50e-08 ***\n",
       "cloud_norm_7_rean2 -0.024520     0.023671  -1.036 0.300269    \n",
       "rhum_rean2          0.024692     0.006800   3.631 0.000282 ***\n",
       "rhum_norm_7_rean2   0.080261     0.036070   2.225 0.026072 *  \n",
       "wind_rean2          0.137825     0.020981   6.569 5.07e-11 ***\n",
       "wind_norm_7_rean2  -0.296424     0.184313  -1.608 0.107778    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.76 on 4640478 degrees of freedom\n",
       "  (2706849 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.284   Adjusted R-squared: 0.2742 \n",
       "Multiple R-squared(proj model): 0.0001234   Adjusted R-squared: -0.01359 \n",
       "F-statistic(full model, *iid*):28.92 on 63643 and 4640478 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 43.46 on 10 and 1118 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #linear tmin \n",
    "# linear_tmin <- felm(formula = sleep_duration_minutes ~ tmin + tmin_norm_w7 +\n",
    "#                                           prcp + prcp_norm_w7 +  \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "# summary(linear_tmin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmax + tmax_norm_w7 +      prcp + prcp_norm_w7 + cloud_rean2 + cloud_norm_7_rean2 +      rhum_rean2 + rhum_norm_7_rean2 + wind_rean2 + wind_norm_7_rean2 |      userID + unique_day_no + stateyrmn | 0 | state, data = Climate_Controls_DF,      na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-386.10  -49.11   -2.42   45.57  403.67 \n",
       "\n",
       "Coefficients:\n",
       "                    Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmax               -0.263840     0.018113 -14.567  < 2e-16 ***\n",
       "tmax_norm_w7       -0.119071     0.080951  -1.471  0.14132    \n",
       "prcp                0.220687     0.109425   2.017  0.04372 *  \n",
       "prcp_norm_w7        2.105009     0.956681   2.200  0.02778 *  \n",
       "cloud_rean2         0.010528     0.002079   5.064 4.11e-07 ***\n",
       "cloud_norm_7_rean2 -0.059883     0.021669  -2.763  0.00572 ** \n",
       "rhum_rean2         -0.001497     0.006804  -0.220  0.82583    \n",
       "rhum_norm_7_rean2   0.084262     0.035766   2.356  0.01848 *  \n",
       "wind_rean2          0.122615     0.020217   6.065 1.32e-09 ***\n",
       "wind_norm_7_rean2  -0.308432     0.157064  -1.964  0.04956 *  \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.57 on 4968482 degrees of freedom\n",
       "  (2381760 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.283   Adjusted R-squared: 0.2742 \n",
       "Multiple R-squared(proj model): 0.0001495   Adjusted R-squared: -0.01207 \n",
       "F-statistic(full model, *iid*):32.29 on 60728 and 4968482 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model):  42.4 on 10 and 1092 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #linear tmax (no diurnal temperature range)\n",
    "# linear_tmax <- felm(formula = sleep_duration_minutes ~ tmax + tmax_norm_w7 +\n",
    "#                                           prcp + prcp_norm_w7 +  \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "# summary(linear_tmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmin + tmin_norm_w7 +      tmax + tmax_norm_w7 + prcp + prcp_norm_w7 + cloud_rean2 +      cloud_norm_7_rean2 + rhum_rean2 + rhum_norm_7_rean2 + wind_rean2 +      wind_norm_7_rean2 | userID + unique_day_no + stateyrmn |      0 | state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-384.37  -49.12   -2.32   45.75  407.69 \n",
       "\n",
       "Coefficients:\n",
       "                     Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin               -0.0871739    0.0328580  -2.653  0.00798 ** \n",
       "tmin_norm_w7       -0.2329876    0.1510755  -1.542  0.12303    \n",
       "tmax               -0.2075398    0.0226437  -9.165  < 2e-16 ***\n",
       "tmax_norm_w7        0.0552583    0.1469579   0.376  0.70691    \n",
       "prcp                0.4835500    0.1204941   4.013 5.99e-05 ***\n",
       "prcp_norm_w7        2.1182315    1.0849373   1.952  0.05089 .  \n",
       "cloud_rean2         0.0101481    0.0023800   4.264 2.01e-05 ***\n",
       "cloud_norm_7_rean2 -0.0389083    0.0239672  -1.623  0.10450    \n",
       "rhum_rean2          0.0005094    0.0075284   0.068  0.94605    \n",
       "rhum_norm_7_rean2   0.1037366    0.0395727   2.621  0.00876 ** \n",
       "wind_rean2          0.1275381    0.0218809   5.829 5.59e-09 ***\n",
       "wind_norm_7_rean2  -0.2956292    0.1875531  -1.576  0.11497    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.71 on 4345352 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2842   Adjusted R-squared: 0.2744 \n",
       "Multiple R-squared(proj model): 0.0001526   Adjusted R-squared: -0.01361 \n",
       "F-statistic(full model, *iid*):28.84 on 59818 and 4345352 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 43.08 on 12 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #linear tmin and tmax (no trange)\n",
    "# linear_tcomb <- felm(formula = sleep_duration_minutes ~ tmin + tmin_norm_w7 + tmax + tmax_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 +  \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "# summary(linear_tcomb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{Daily minimum vs. maximum temperature (linear FE models)} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lccc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{3}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-4} \n",
      " & Linear TMIN GHCND & Linear TMAX GHCND & Linear TMIN & TMAX \\\\ \n",
      "\\\\[-1.8ex] & (1) & (2) & (3)\\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " tmin & $-$0.226$^{***}$ &  & $-$0.087$^{***}$ \\\\ \n",
      "  & (0.026) &  & (0.033) \\\\ \n",
      "  tmin\\_norm\\_w7 & $-$0.222$^{***}$ &  & $-$0.233 \\\\ \n",
      "  & (0.079) &  & (0.151) \\\\ \n",
      "  tmax &  & $-$0.264$^{***}$ & $-$0.208$^{***}$ \\\\ \n",
      "  &  & (0.018) & (0.023) \\\\ \n",
      "  tmax\\_norm\\_w7 &  & $-$0.119 & 0.055 \\\\ \n",
      "  &  & (0.081) & (0.147) \\\\ \n",
      "  prcp & 0.628$^{***}$ & 0.221$^{**}$ & 0.484$^{***}$ \\\\ \n",
      "  & (0.114) & (0.109) & (0.120) \\\\ \n",
      "  prcp\\_norm\\_w7 & 1.570 & 2.105$^{**}$ & 2.118$^{*}$ \\\\ \n",
      "  & (1.062) & (0.957) & (1.085) \\\\ \n",
      "  cloud\\_rean2 & 0.012$^{***}$ & 0.011$^{***}$ & 0.010$^{***}$ \\\\ \n",
      "  & (0.002) & (0.002) & (0.002) \\\\ \n",
      "  cloud\\_norm\\_7\\_rean2 & $-$0.025 & $-$0.060$^{***}$ & $-$0.039 \\\\ \n",
      "  & (0.024) & (0.022) & (0.024) \\\\ \n",
      "  rhum\\_rean2 & 0.025$^{***}$ & $-$0.001 & 0.001 \\\\ \n",
      "  & (0.007) & (0.007) & (0.008) \\\\ \n",
      "  rhum\\_norm\\_7\\_rean2 & 0.080$^{**}$ & 0.084$^{**}$ & 0.104$^{***}$ \\\\ \n",
      "  & (0.036) & (0.036) & (0.040) \\\\ \n",
      "  wind\\_rean2 & 0.138$^{***}$ & 0.123$^{***}$ & 0.128$^{***}$ \\\\ \n",
      "  & (0.021) & (0.020) & (0.022) \\\\ \n",
      "  wind\\_norm\\_7\\_rean2 & $-$0.296 & $-$0.308$^{**}$ & $-$0.296 \\\\ \n",
      "  & (0.184) & (0.157) & (0.188) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes & Yes & Yes \\\\ \n",
      "Date FE & Yes & Yes & Yes \\\\ \n",
      "State:Month FE & Yes & Yes & Yes \\\\ \n",
      "Observations & 4,704,122 & 5,029,211 & 4,405,171 \\\\ \n",
      "R$^{2}$ & 0.284 & 0.283 & 0.284 \\\\ \n",
      "Adjusted R$^{2}$ & 0.274 & 0.274 & 0.274 \\\\ \n",
      "Residual Std. Error & 77.759 & 77.571 & 77.708 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{3}{r}{Standard errors are in parentheses and are clustered on the 1st admin level} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# invisible(stargazer(linear_tmin, linear_tmax, linear_tcomb,\n",
    "#           type='latex',\n",
    "#           title = \"Daily minimum vs. maximum temperature (linear FE models)\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Linear TMIN GHCND', 'Linear TMAX GHCND', 'Linear TMIN & TMAX'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the 1st admin level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Compare Tmax vs. Tmin specifications (with trange included as a control)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmin + tmin_norm_w7 +      trange + trange_norm_w7 + prcp + prcp_norm_w7 + cloud_rean2 +      cloud_norm_7_rean2 + rhum_rean2 + rhum_norm_7_rean2 + wind_rean2 +      wind_norm_7_rean2 | userID + unique_day_no + stateyrmn |      0 | state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-384.37  -49.12   -2.32   45.75  407.69 \n",
       "\n",
       "Coefficients:\n",
       "                     Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin               -0.2947137    0.0269365 -10.941  < 2e-16 ***\n",
       "tmin_norm_w7       -0.1777292    0.0853306  -2.083  0.03727 *  \n",
       "trange             -0.2075398    0.0226437  -9.165  < 2e-16 ***\n",
       "trange_norm_w7      0.0552583    0.1469579   0.376  0.70691    \n",
       "prcp                0.4835500    0.1204941   4.013 5.99e-05 ***\n",
       "prcp_norm_w7        2.1182315    1.0849373   1.952  0.05089 .  \n",
       "cloud_rean2         0.0101481    0.0023800   4.264 2.01e-05 ***\n",
       "cloud_norm_7_rean2 -0.0389083    0.0239672  -1.623  0.10450    \n",
       "rhum_rean2          0.0005094    0.0075284   0.068  0.94605    \n",
       "rhum_norm_7_rean2   0.1037366    0.0395727   2.621  0.00876 ** \n",
       "wind_rean2          0.1275381    0.0218809   5.829 5.59e-09 ***\n",
       "wind_norm_7_rean2  -0.2956292    0.1875531  -1.576  0.11497    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.71 on 4345352 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2842   Adjusted R-squared: 0.2744 \n",
       "Multiple R-squared(proj model): 0.0001526   Adjusted R-squared: -0.01361 \n",
       "F-statistic(full model, *iid*):28.84 on 59818 and 4345352 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 43.08 on 12 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #Tmin + trange (original specification)\n",
    "# linear_tmin_orig <- felm(formula = sleep_duration_minutes ~ tmin + tmin_norm_w7 +\n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 +  \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "# summary(linear_tmin_orig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmax + tmax_norm_w7 +      trange + trange_norm_w7 + prcp + prcp_norm_w7 + cloud_rean2 +      cloud_norm_7_rean2 + rhum_rean2 + rhum_norm_7_rean2 + wind_rean2 +      wind_norm_7_rean2 | userID + unique_day_no + stateyrmn |      0 | state, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-384.37  -49.12   -2.32   45.75  407.69 \n",
       "\n",
       "Coefficients:\n",
       "                     Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmax               -0.2947137    0.0269365 -10.941  < 2e-16 ***\n",
       "tmax_norm_w7       -0.1777292    0.0853306  -2.083  0.03727 *  \n",
       "trange              0.0871739    0.0328580   2.653  0.00798 ** \n",
       "trange_norm_w7      0.2329876    0.1510755   1.542  0.12303    \n",
       "prcp                0.4835500    0.1204941   4.013 5.99e-05 ***\n",
       "prcp_norm_w7        2.1182315    1.0849373   1.952  0.05089 .  \n",
       "cloud_rean2         0.0101481    0.0023800   4.264 2.01e-05 ***\n",
       "cloud_norm_7_rean2 -0.0389083    0.0239672  -1.623  0.10450    \n",
       "rhum_rean2          0.0005094    0.0075284   0.068  0.94605    \n",
       "rhum_norm_7_rean2   0.1037366    0.0395727   2.621  0.00876 ** \n",
       "wind_rean2          0.1275381    0.0218809   5.829 5.59e-09 ***\n",
       "wind_norm_7_rean2  -0.2956292    0.1875531  -1.576  0.11497    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.71 on 4345352 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2842   Adjusted R-squared: 0.2744 \n",
       "Multiple R-squared(proj model): 0.0001526   Adjusted R-squared: -0.01361 \n",
       "F-statistic(full model, *iid*):28.84 on 59818 and 4345352 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 43.08 on 12 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #Tmax + trange (alternative specification)\n",
    "# linear_tmax_full <- felm(formula = sleep_duration_minutes ~ tmax + tmax_norm_w7 +\n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 +  \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "# summary(linear_tmax_full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{Table 45b: Alternative Temperature Vars: Minimum vs. Max Daily Temperature (WITH diurnal temp range)} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lcc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{2}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-3} \n",
      " & Linear TMIN GHCND & Linear TMAX GHCND \\\\ \n",
      "\\\\[-1.8ex] & (1) & (2)\\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " tmin & $-$0.295$^{***}$ &  \\\\ \n",
      "  & (0.027) &  \\\\ \n",
      "  tmin\\_norm\\_w7 & $-$0.178$^{**}$ &  \\\\ \n",
      "  & (0.085) &  \\\\ \n",
      "  tmax &  & $-$0.295$^{***}$ \\\\ \n",
      "  &  & (0.027) \\\\ \n",
      "  tmax\\_norm\\_w7 &  & $-$0.178$^{**}$ \\\\ \n",
      "  &  & (0.085) \\\\ \n",
      "  trange & $-$0.208$^{***}$ & 0.087$^{***}$ \\\\ \n",
      "  & (0.023) & (0.033) \\\\ \n",
      "  trange\\_norm\\_w7 & 0.055 & 0.233 \\\\ \n",
      "  & (0.147) & (0.151) \\\\ \n",
      "  prcp & 0.484$^{***}$ & 0.484$^{***}$ \\\\ \n",
      "  & (0.120) & (0.120) \\\\ \n",
      "  prcp\\_norm\\_w7 & 2.118$^{*}$ & 2.118$^{*}$ \\\\ \n",
      "  & (1.085) & (1.085) \\\\ \n",
      "  cloud\\_rean2 & 0.010$^{***}$ & 0.010$^{***}$ \\\\ \n",
      "  & (0.002) & (0.002) \\\\ \n",
      "  cloud\\_norm\\_7\\_rean2 & $-$0.039 & $-$0.039 \\\\ \n",
      "  & (0.024) & (0.024) \\\\ \n",
      "  rhum\\_rean2 & 0.001 & 0.001 \\\\ \n",
      "  & (0.008) & (0.008) \\\\ \n",
      "  rhum\\_norm\\_7\\_rean2 & 0.104$^{***}$ & 0.104$^{***}$ \\\\ \n",
      "  & (0.040) & (0.040) \\\\ \n",
      "  wind\\_rean2 & 0.128$^{***}$ & 0.128$^{***}$ \\\\ \n",
      "  & (0.022) & (0.022) \\\\ \n",
      "  wind\\_norm\\_7\\_rean2 & $-$0.296 & $-$0.296 \\\\ \n",
      "  & (0.188) & (0.188) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes & Yes \\\\ \n",
      "Date FE & Yes & Yes \\\\ \n",
      "State:Month FE & Yes & Yes \\\\ \n",
      "Observations & 4,405,171 & 4,405,171 \\\\ \n",
      "R$^{2}$ & 0.284 & 0.284 \\\\ \n",
      "Adjusted R$^{2}$ & 0.274 & 0.274 \\\\ \n",
      "Residual Std. Error & 77.708 & 77.708 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{2}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{2}{r}{Standard errors are in parentheses and are clustered on the 1st admin level} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# invisible(stargazer(linear_tmin_orig, linear_tmax_full,\n",
    "#           type='latex',\n",
    "#           title = \"Table 45b: Alternative Temperature Vars: Minimum vs. Max Daily Temperature (WITH diurnal temp range)\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Linear TMIN GHCND', 'Linear TMAX GHCND'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the 1st admin level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Alternative Clustering Specifications: Linear Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ tmin + tmin_norm_w7 +      trange + trange_norm_w7 + prcp + prcp_norm_w7 + cloud_rean2 +      cloud_norm_7_rean2 + rhum_rean2 + rhum_norm_7_rean2 + wind_rean2 +      wind_norm_7_rean2 | userID + unique_day_no + stateyrmn |      0 | state + unique_day_no, data = Climate_Controls_DF, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-384.35  -49.13   -2.32   45.76  407.67 \n",
       "\n",
       "Coefficients:\n",
       "                    Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin               -0.294272     0.053189  -5.533 3.16e-08 ***\n",
       "tmin_norm_w7       -0.164135     0.110836  -1.481  0.13864    \n",
       "trange             -0.207180     0.050310  -4.118 3.82e-05 ***\n",
       "trange_norm_w7      0.037610     0.176846   0.213  0.83158    \n",
       "prcp                0.482288     0.211766   2.277  0.02276 *  \n",
       "prcp_norm_w7        2.250506     1.645981   1.367  0.17154    \n",
       "cloud_rean2         0.010141     0.005202   1.950  0.05123 .  \n",
       "cloud_norm_7_rean2 -0.041776     0.045723  -0.914  0.36089    \n",
       "rhum_rean2          0.000686     0.015176   0.045  0.96395    \n",
       "rhum_norm_7_rean2   0.099271     0.065059   1.526  0.12704    \n",
       "wind_rean2          0.127602     0.047129   2.708  0.00678 ** \n",
       "wind_norm_7_rean2  -0.270277     0.254463  -1.062  0.28817    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.72 on 4353298 degrees of freedom\n",
       "  (3063777 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2842   Adjusted R-squared: 0.2742 \n",
       "Multiple R-squared(proj model): 0.0001518   Adjusted R-squared: -0.01376 \n",
       "F-statistic(full model, *iid*):28.53 on 60563 and 4353298 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 8.553 on 12 and 1193 DF, p-value: 9.261e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #Robustness: cluster on admin 1 & time (day of study) linear spec\n",
    "# linear_t_space_time <- felm(formula = sleep_duration_minutes ~ tmin + tmin_norm_w7 +\n",
    "#                                           trange + trange_norm_w7 +\n",
    "#                                           prcp + prcp_norm_w7 +  \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state + unique_day_no, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "# summary(linear_t_space_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{Alternative Clustering Specifications Linear models} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lcc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{2}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-3} \n",
      " & Cluster on Admin 1 Region & Cluster on Admin 1 Region and Date of Study \\\\ \n",
      "\\\\[-1.8ex] & (1) & (2)\\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " tmin & $-$0.294$^{***}$ & $-$0.294$^{***}$ \\\\ \n",
      "  & (0.027) & (0.053) \\\\ \n",
      "  tmin\\_norm\\_w7 & $-$0.164$^{**}$ & $-$0.164 \\\\ \n",
      "  & (0.079) & (0.111) \\\\ \n",
      "  prcp & 0.482$^{***}$ & 0.482$^{**}$ \\\\ \n",
      "  & (0.120) & (0.212) \\\\ \n",
      "  prcp\\_norm\\_w7 & 2.251$^{**}$ & 2.251 \\\\ \n",
      "  & (1.132) & (1.646) \\\\ \n",
      "  trange & $-$0.207$^{***}$ & $-$0.207$^{***}$ \\\\ \n",
      "  & (0.023) & (0.050) \\\\ \n",
      "  trange\\_norm\\_w7 & 0.038 & 0.038 \\\\ \n",
      "  & (0.145) & (0.177) \\\\ \n",
      "  cloud\\_rean2 & 0.010$^{***}$ & 0.010$^{*}$ \\\\ \n",
      "  & (0.002) & (0.005) \\\\ \n",
      "  cloud\\_norm\\_7\\_rean2 & $-$0.042$^{*}$ & $-$0.042 \\\\ \n",
      "  & (0.024) & (0.046) \\\\ \n",
      "  rhum\\_rean2 & 0.001 & 0.001 \\\\ \n",
      "  & (0.008) & (0.015) \\\\ \n",
      "  rhum\\_norm\\_7\\_rean2 & 0.099$^{***}$ & 0.099 \\\\ \n",
      "  & (0.038) & (0.065) \\\\ \n",
      "  wind\\_rean2 & 0.128$^{***}$ & 0.128$^{***}$ \\\\ \n",
      "  & (0.022) & (0.047) \\\\ \n",
      "  wind\\_norm\\_7\\_rean2 & $-$0.270 & $-$0.270 \\\\ \n",
      "  & (0.193) & (0.254) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes & Yes \\\\ \n",
      "Date FE & Yes & Yes \\\\ \n",
      "State:Month FE & Yes & Yes \\\\ \n",
      "Observations & 4,413,862 & 4,413,862 \\\\ \n",
      "R$^{2}$ & 0.284 & 0.284 \\\\ \n",
      "Adjusted R$^{2}$ & 0.274 & 0.274 \\\\ \n",
      "Residual Std. Error & 77.716 & 77.716 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{2}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{2}{r}{Standard errors are in parentheses and are clustered at the specified levels} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# invisible(stargazer(linear_t, linear_t_space_time,\n",
    "#           type='latex',\n",
    "#           title = \"Alternative Clustering Specifications Linear models\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Cluster on Admin 1 Region', 'Cluster on Admin 1 Region and Date of Study'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"State:Month FE\", \"Yes\", \"Yes\", \"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered at the specified levels'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Alternative Clustering Specifications: Binned Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ CUTTMIN + tmin_norm_w7 +      CUTPRCP + prcp_norm_w7 + CUTTRANGE + trange_norm_w7 + CUTCLOUD_rean2 +      cloud_norm_7_rean2 + CUTRHUM_rean2 + rhum_norm_7_rean2 +      CUTWIND_rean2 + wind_norm_7_rean2 | userID + unique_day_no +      stateyrmn | 0 | state + unique_day_no, data = Climate_Controls_DF,      na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-383.32  -49.12   -2.31   45.76  408.97 \n",
       "\n",
       "Coefficients:\n",
       "                        Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "CUTTMIN(-Inf,-20]        3.30787      2.22219   1.489 0.136603    \n",
       "CUTTMIN(-20,-15]         3.11775      1.38176   2.256 0.024048 *  \n",
       "CUTTMIN(-15,-10]         2.00341      1.08625   1.844 0.065136 .  \n",
       "CUTTMIN(-10,-5]          0.84929      0.76987   1.103 0.269954    \n",
       "CUTTMIN(-5,0]            0.42534      0.64395   0.661 0.508923    \n",
       "CUTTMIN(0,5]             0.65287      0.36856   1.771 0.076490 .  \n",
       "CUTTMIN(10,15]          -1.95615      0.39413  -4.963 6.94e-07 ***\n",
       "CUTTMIN(15,20]          -4.36808      0.57981  -7.534 4.94e-14 ***\n",
       "CUTTMIN(20,25]          -6.35805      0.76948  -8.263  < 2e-16 ***\n",
       "CUTTMIN(25,30]          -7.24025      1.06642  -6.789 1.13e-11 ***\n",
       "CUTTMIN(30, Inf]       -11.35976      1.98366  -5.727 1.02e-08 ***\n",
       "tmin_norm_w7            -0.19342      0.10801  -1.791 0.073324 .  \n",
       "CUTPRCP(0,1]             0.30077      0.22762   1.321 0.186387    \n",
       "CUTPRCP(1,2]             1.22089      0.50714   2.407 0.016067 *  \n",
       "CUTPRCP(2,3]             1.31648      0.49148   2.679 0.007393 ** \n",
       "CUTPRCP(3, Inf]          2.10474      0.64105   3.283 0.001026 ** \n",
       "prcp_norm_w7             2.30245      1.66015   1.387 0.165473    \n",
       "CUTTRANGE(-Inf,0]        2.69298      1.21182   2.222 0.026265 *  \n",
       "CUTTRANGE(0,5]           0.26814      0.22937   1.169 0.242400    \n",
       "CUTTRANGE(10,15]        -0.58354      0.22269  -2.620 0.008783 ** \n",
       "CUTTRANGE(15,20]        -2.00157      0.59167  -3.383 0.000717 ***\n",
       "CUTTRANGE(20, Inf]      -2.04724      0.85387  -2.398 0.016503 *  \n",
       "trange_norm_w7          -0.04832      0.17517  -0.276 0.782650    \n",
       "CUTCLOUD_rean2(0,20]     0.49139      0.48033   1.023 0.306291    \n",
       "CUTCLOUD_rean2(20,40]    0.35640      0.53183   0.670 0.502772    \n",
       "CUTCLOUD_rean2(40,60]    0.62198      0.56323   1.104 0.269465    \n",
       "CUTCLOUD_rean2(60,80]    0.97123      0.58950   1.648 0.099448 .  \n",
       "CUTCLOUD_rean2(80,100]   1.59944      0.68552   2.333 0.019639 *  \n",
       "cloud_norm_7_rean2      -0.03319      0.04429  -0.749 0.453683    \n",
       "CUTRHUM_rean2(0,20]     -4.61724      1.35480  -3.408 0.000654 ***\n",
       "CUTRHUM_rean2(20,40]    -1.61132      0.79413  -2.029 0.042454 *  \n",
       "CUTRHUM_rean2(40,60]    -0.01433      0.39924  -0.036 0.971371    \n",
       "CUTRHUM_rean2(80,100]   -0.32928      0.26176  -1.258 0.208408    \n",
       "rhum_norm_7_rean2        0.09525      0.06309   1.510 0.131066    \n",
       "CUTWIND_rean2(5,10]      0.47956      0.18736   2.559 0.010483 *  \n",
       "CUTWIND_rean2(10,Inf]    0.77426      0.48558   1.595 0.110821    \n",
       "wind_norm_7_rean2       -0.29749      0.25676  -1.159 0.246608    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.72 on 4353273 degrees of freedom\n",
       "  (3063777 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2842   Adjusted R-squared: 0.2742 \n",
       "Multiple R-squared(proj model): 0.0001854   Adjusted R-squared: -0.01373 \n",
       "F-statistic(full model, *iid*):28.53 on 60588 and 4353273 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 10.29 on 37 and 1193 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #Binned GHCND - tmin (Robustness: Cluster on Space + Time)\n",
    "# flex_t_binned_GHCND_space_time <- felm(formula = sleep_duration_minutes ~ CUTTMIN + tmin_norm_w7 + \n",
    "#                                           CUTPRCP + prcp_norm_w7 + \n",
    "#                                           CUTTRANGE + trange_norm_w7 + \n",
    "#                                           CUTCLOUD_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           CUTRHUM_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           CUTWIND_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state + unique_day_no, \n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(flex_t_binned_GHCND_space_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{X. Flexible Spec with alternative clustering specification} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lcc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{2}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-3} \n",
      " & Cluster on Admin 1 & Cluster on Admin 1 and Date of Study \\\\ \n",
      "\\\\[-1.8ex] & (1) & (2)\\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " CUTTMIN(-Inf,-20] & 3.308$^{**}$ & 3.308 \\\\ \n",
      "  & (1.456) & (2.222) \\\\ \n",
      "  CUTTMIN(-20,-15] & 3.118$^{***}$ & 3.118$^{**}$ \\\\ \n",
      "  & (0.863) & (1.382) \\\\ \n",
      "  CUTTMIN(-15,-10] & 2.003$^{***}$ & 2.003$^{*}$ \\\\ \n",
      "  & (0.670) & (1.086) \\\\ \n",
      "  CUTTMIN(-10,-5] & 0.849$^{*}$ & 0.849 \\\\ \n",
      "  & (0.476) & (0.770) \\\\ \n",
      "  CUTTMIN(-5,0] & 0.425 & 0.425 \\\\ \n",
      "  & (0.392) & (0.644) \\\\ \n",
      "  CUTTMIN(0,5] & 0.653$^{***}$ & 0.653$^{*}$ \\\\ \n",
      "  & (0.246) & (0.369) \\\\ \n",
      "  CUTTMIN(10,15] & $-$1.956$^{***}$ & $-$1.956$^{***}$ \\\\ \n",
      "  & (0.220) & (0.394) \\\\ \n",
      "  CUTTMIN(15,20] & $-$4.368$^{***}$ & $-$4.368$^{***}$ \\\\ \n",
      "  & (0.333) & (0.580) \\\\ \n",
      "  CUTTMIN(20,25] & $-$6.358$^{***}$ & $-$6.358$^{***}$ \\\\ \n",
      "  & (0.448) & (0.769) \\\\ \n",
      "  CUTTMIN(25,30] & $-$7.240$^{***}$ & $-$7.240$^{***}$ \\\\ \n",
      "  & (0.604) & (1.066) \\\\ \n",
      "  CUTTMIN(30, Inf] & $-$11.360$^{***}$ & $-$11.360$^{***}$ \\\\ \n",
      "  & (1.766) & (1.984) \\\\ \n",
      "  tmin\\_norm\\_w7 & $-$0.193$^{***}$ & $-$0.193$^{*}$ \\\\ \n",
      "  & (0.074) & (0.108) \\\\ \n",
      "  CUTPRCP(0,1] & 0.301$^{***}$ & 0.301 \\\\ \n",
      "  & (0.105) & (0.228) \\\\ \n",
      "  CUTPRCP(1,2] & 1.221$^{***}$ & 1.221$^{**}$ \\\\ \n",
      "  & (0.290) & (0.507) \\\\ \n",
      "  CUTPRCP(2,3] & 1.316$^{***}$ & 1.316$^{***}$ \\\\ \n",
      "  & (0.350) & (0.491) \\\\ \n",
      "  CUTPRCP(3, Inf] & 2.105$^{***}$ & 2.105$^{***}$ \\\\ \n",
      "  & (0.545) & (0.641) \\\\ \n",
      "  prcp\\_norm\\_w7 & 2.302$^{**}$ & 2.302 \\\\ \n",
      "  & (1.145) & (1.660) \\\\ \n",
      "  CUTTRANGE(-Inf,0] & 2.693$^{**}$ & 2.693$^{**}$ \\\\ \n",
      "  & (1.214) & (1.212) \\\\ \n",
      "  CUTTRANGE(0,5] & 0.268 & 0.268 \\\\ \n",
      "  & (0.175) & (0.229) \\\\ \n",
      "  CUTTRANGE(10,15] & $-$0.584$^{***}$ & $-$0.584$^{***}$ \\\\ \n",
      "  & (0.113) & (0.223) \\\\ \n",
      "  CUTTRANGE(15,20] & $-$2.002$^{***}$ & $-$2.002$^{***}$ \\\\ \n",
      "  & (0.299) & (0.592) \\\\ \n",
      "  CUTTRANGE(20, Inf] & $-$2.047$^{***}$ & $-$2.047$^{**}$ \\\\ \n",
      "  & (0.726) & (0.854) \\\\ \n",
      "  trange\\_norm\\_w7 & $-$0.048 & $-$0.048 \\\\ \n",
      "  & (0.143) & (0.175) \\\\ \n",
      "  CUTCLOUD\\_rean2(0,20] & 0.491 & 0.491 \\\\ \n",
      "  & (0.437) & (0.480) \\\\ \n",
      "  CUTCLOUD\\_rean2(20,40] & 0.356 & 0.356 \\\\ \n",
      "  & (0.459) & (0.532) \\\\ \n",
      "  CUTCLOUD\\_rean2(40,60] & 0.622 & 0.622 \\\\ \n",
      "  & (0.451) & (0.563) \\\\ \n",
      "  CUTCLOUD\\_rean2(60,80] & 0.971$^{**}$ & 0.971$^{*}$ \\\\ \n",
      "  & (0.481) & (0.590) \\\\ \n",
      "  CUTCLOUD\\_rean2(80,100] & 1.599$^{***}$ & 1.599$^{**}$ \\\\ \n",
      "  & (0.456) & (0.686) \\\\ \n",
      "  cloud\\_norm\\_7\\_rean2 & $-$0.033 & $-$0.033 \\\\ \n",
      "  & (0.023) & (0.044) \\\\ \n",
      "  CUTRHUM\\_rean2(0,20] & $-$4.617$^{***}$ & $-$4.617$^{***}$ \\\\ \n",
      "  & (1.221) & (1.355) \\\\ \n",
      "  CUTRHUM\\_rean2(20,40] & $-$1.611$^{***}$ & $-$1.611$^{**}$ \\\\ \n",
      "  & (0.615) & (0.794) \\\\ \n",
      "  CUTRHUM\\_rean2(40,60] & $-$0.014 & $-$0.014 \\\\ \n",
      "  & (0.190) & (0.399) \\\\ \n",
      "  CUTRHUM\\_rean2(80,100] & $-$0.329$^{**}$ & $-$0.329 \\\\ \n",
      "  & (0.136) & (0.262) \\\\ \n",
      "  rhum\\_norm\\_7\\_rean2 & 0.095$^{**}$ & 0.095 \\\\ \n",
      "  & (0.038) & (0.063) \\\\ \n",
      "  CUTWIND\\_rean2(5,10] & 0.480$^{***}$ & 0.480$^{**}$ \\\\ \n",
      "  & (0.118) & (0.187) \\\\ \n",
      "  CUTWIND\\_rean2(10,Inf] & 0.774$^{***}$ & 0.774 \\\\ \n",
      "  & (0.232) & (0.486) \\\\ \n",
      "  wind\\_norm\\_7\\_rean2 & $-$0.297 & $-$0.297 \\\\ \n",
      "  & (0.201) & (0.257) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes & Yes \\\\ \n",
      "Date FE & Yes & Yes \\\\ \n",
      "Admin_2:Month FE & Yes & Yes \\\\ \n",
      "Observations & 4,413,862 & 4,413,862 \\\\ \n",
      "R$^{2}$ & 0.284 & 0.284 \\\\ \n",
      "Adjusted R$^{2}$ & 0.274 & 0.274 \\\\ \n",
      "Residual Std. Error & 77.715 & 77.715 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{2}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{2}{r}{Standard errors are in parentheses and are clustered on the specified level} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# #Tables\n",
    "# invisible(stargazer(flex_t_binned_GHCND, flex_t_binned_GHCND_space_time,\n",
    "#           type='latex',\n",
    "#           title = \"X. Flexible Spec with alternative clustering specification\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Cluster on Admin 1','Cluster on Admin 1 and Date of Study'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\",\"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Admin_2:Month FE\", \"Yes\", \"Yes\",\"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the specified level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Alternative Inclusion Criteria: With vs. Without Data from Lower and Upper Middle Income Countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = sleep_duration_minutes ~ CUTTMIN + tmin_norm_w7 +      CUTPRCP + prcp_norm_w7 + CUTTRANGE + trange_norm_w7 + CUTCLOUD_rean2 +      cloud_norm_7_rean2 + CUTRHUM_rean2 + rhum_norm_7_rean2 +      CUTWIND_rean2 + wind_norm_7_rean2 | userID + unique_day_no +      stateyrmn | 0 | state, data = Weather, na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-361.32  -49.03   -2.38   45.58  409.33 \n",
       "\n",
       "Coefficients:\n",
       "                        Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "CUTTMIN(-Inf,-20]       7.310396     1.204794   6.068 1.30e-09 ***\n",
       "CUTTMIN(-20,-15]        2.514155     1.112167   2.261 0.023785 *  \n",
       "CUTTMIN(-15,-10]        1.996507     0.793407   2.516 0.011857 *  \n",
       "CUTTMIN(-10,-5]         0.323965     0.488819   0.663 0.507491    \n",
       "CUTTMIN(-5,0]           0.177326     0.390774   0.454 0.649986    \n",
       "CUTTMIN(0,5]            0.526306     0.251153   2.096 0.036121 *  \n",
       "CUTTMIN(10,15]         -1.780402     0.231383  -7.695 1.42e-14 ***\n",
       "CUTTMIN(15,20]         -4.088451     0.352474 -11.599  < 2e-16 ***\n",
       "CUTTMIN(20,25]         -6.118332     0.478611 -12.784  < 2e-16 ***\n",
       "CUTTMIN(25,30]         -6.953968     0.649311 -10.710  < 2e-16 ***\n",
       "CUTTMIN(30, Inf]       -9.428998     1.760067  -5.357 8.45e-08 ***\n",
       "tmin_norm_w7           -0.186114     0.089506  -2.079 0.037586 *  \n",
       "CUTPRCP(0,1]            0.308863     0.112026   2.757 0.005832 ** \n",
       "CUTPRCP(1,2]            1.324414     0.299106   4.428 9.52e-06 ***\n",
       "CUTPRCP(2,3]            1.320192     0.367055   3.597 0.000322 ***\n",
       "CUTPRCP(3, Inf]         2.149855     0.569455   3.775 0.000160 ***\n",
       "prcp_norm_w7            2.337225     1.144086   2.043 0.041065 *  \n",
       "CUTTRANGE(-Inf,0]       3.031453     1.339631   2.263 0.023642 *  \n",
       "CUTTRANGE(0,5]          0.204234     0.181070   1.128 0.259349    \n",
       "CUTTRANGE(10,15]       -0.551496     0.115961  -4.756 1.98e-06 ***\n",
       "CUTTRANGE(15,20]       -1.995136     0.322952  -6.178 6.50e-10 ***\n",
       "CUTTRANGE(20, Inf]     -1.817473     0.716936  -2.535 0.011243 *  \n",
       "trange_norm_w7          0.002796     0.152749   0.018 0.985396    \n",
       "CUTCLOUD_rean2(0,20]    0.695267     0.461331   1.507 0.131788    \n",
       "CUTCLOUD_rean2(20,40]   0.509568     0.489871   1.040 0.298243    \n",
       "CUTCLOUD_rean2(40,60]   0.782005     0.479720   1.630 0.103074    \n",
       "CUTCLOUD_rean2(60,80]   1.135069     0.518154   2.191 0.028481 *  \n",
       "CUTCLOUD_rean2(80,100]  1.758200     0.483083   3.640 0.000273 ***\n",
       "cloud_norm_7_rean2     -0.036126     0.024676  -1.464 0.143189    \n",
       "CUTRHUM_rean2(0,20]    -3.775817     1.780228  -2.121 0.033924 *  \n",
       "CUTRHUM_rean2(20,40]   -0.994612     0.634228  -1.568 0.116829    \n",
       "CUTRHUM_rean2(40,60]   -0.021211     0.195129  -0.109 0.913438    \n",
       "CUTRHUM_rean2(80,100]  -0.340116     0.138557  -2.455 0.014100 *  \n",
       "rhum_norm_7_rean2       0.114595     0.041748   2.745 0.006052 ** \n",
       "CUTWIND_rean2(5,10]     0.438588     0.129332   3.391 0.000696 ***\n",
       "CUTWIND_rean2(10,Inf]   0.821298     0.238988   3.437 0.000589 ***\n",
       "wind_norm_7_rean2      -0.317120     0.198759  -1.595 0.110601    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 77.5 on 4067079 degrees of freedom\n",
       "  (2443289 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.2857   Adjusted R-squared: 0.2771 \n",
       "Multiple R-squared(proj model): 0.0001764   Adjusted R-squared: -0.01186 \n",
       "F-statistic(full model, *iid*):33.23 on 48964 and 4067079 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 17.69 on 37 and 466 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #High Income Only Robustness Check\n",
    "# #Dropping all LMIC and UMIC countries (High income only)\n",
    "# Weather<-as.data.table(Climate_Controls_DF)\n",
    "# Weather<-Weather[income_cat==\"High income\"] #constrain analysis to high income settings \n",
    "# #Binned GHCND - tmin High Income Countries\n",
    "# flex_t_binned_GHCND_HIC <- felm(formula = sleep_duration_minutes ~ CUTTMIN + tmin_norm_w7 + \n",
    "#                                           CUTPRCP + prcp_norm_w7 + \n",
    "#                                           CUTTRANGE + trange_norm_w7 + \n",
    "#                                           CUTCLOUD_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           CUTRHUM_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           CUTWIND_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state, \n",
    "#                 data = Weather, na.action = na.omit) \n",
    "\n",
    "# summary(flex_t_binned_GHCND_HIC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{X. Flexible Spec With/Without Data from Lower and Upper Middle Income Countries} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lcc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{2}{c}{Dependent Variable: Sleep Duration (Minutes)} \\\\ \n",
      "\\cline{2-3} \n",
      " & Flexible TMIN All Countries & Flexible TMIN Only High Income \\\\ \n",
      "\\\\[-1.8ex] & (1) & (2)\\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " CUTTMIN(-Inf,-20] & 3.316$^{**}$ & 7.310$^{***}$ \\\\ \n",
      "  & (1.456) & (1.205) \\\\ \n",
      "  CUTTMIN(-20,-15] & 3.093$^{***}$ & 2.514$^{**}$ \\\\ \n",
      "  & (0.866) & (1.112) \\\\ \n",
      "  CUTTMIN(-15,-10] & 2.018$^{***}$ & 1.997$^{**}$ \\\\ \n",
      "  & (0.670) & (0.793) \\\\ \n",
      "  CUTTMIN(-10,-5] & 0.867$^{*}$ & 0.324 \\\\ \n",
      "  & (0.477) & (0.489) \\\\ \n",
      "  CUTTMIN(-5,0] & 0.427 & 0.177 \\\\ \n",
      "  & (0.392) & (0.391) \\\\ \n",
      "  CUTTMIN(0,5] & 0.658$^{***}$ & 0.526$^{**}$ \\\\ \n",
      "  & (0.248) & (0.251) \\\\ \n",
      "  CUTTMIN(10,15] & $-$1.956$^{***}$ & $-$1.780$^{***}$ \\\\ \n",
      "  & (0.219) & (0.231) \\\\ \n",
      "  CUTTMIN(15,20] & $-$4.379$^{***}$ & $-$4.088$^{***}$ \\\\ \n",
      "  & (0.334) & (0.352) \\\\ \n",
      "  CUTTMIN(20,25] & $-$6.397$^{***}$ & $-$6.118$^{***}$ \\\\ \n",
      "  & (0.450) & (0.479) \\\\ \n",
      "  CUTTMIN(25,30] & $-$7.243$^{***}$ & $-$6.954$^{***}$ \\\\ \n",
      "  & (0.605) & (0.649) \\\\ \n",
      "  CUTTMIN(30, Inf] & $-$10.766$^{***}$ & $-$9.429$^{***}$ \\\\ \n",
      "  & (1.769) & (1.760) \\\\ \n",
      "  tmin\\_norm\\_w7 & $-$0.208$^{***}$ & $-$0.186$^{**}$ \\\\ \n",
      "  & (0.079) & (0.090) \\\\ \n",
      "  CUTPRCP(0,1] & 0.298$^{***}$ & 0.309$^{***}$ \\\\ \n",
      "  & (0.106) & (0.112) \\\\ \n",
      "  CUTPRCP(1,2] & 1.218$^{***}$ & 1.324$^{***}$ \\\\ \n",
      "  & (0.292) & (0.299) \\\\ \n",
      "  CUTPRCP(2,3] & 1.330$^{***}$ & 1.320$^{***}$ \\\\ \n",
      "  & (0.350) & (0.367) \\\\ \n",
      "  CUTPRCP(3, Inf] & 2.106$^{***}$ & 2.150$^{***}$ \\\\ \n",
      "  & (0.541) & (0.569) \\\\ \n",
      "  prcp\\_norm\\_w7 & 2.163$^{**}$ & 2.337$^{**}$ \\\\ \n",
      "  & (1.096) & (1.144) \\\\ \n",
      "  CUTTRANGE(-Inf,0] & 2.690$^{**}$ & 3.031$^{**}$ \\\\ \n",
      "  & (1.212) & (1.340) \\\\ \n",
      "  CUTTRANGE(0,5] & 0.261 & 0.204 \\\\ \n",
      "  & (0.176) & (0.181) \\\\ \n",
      "  CUTTRANGE(10,15] & $-$0.590$^{***}$ & $-$0.551$^{***}$ \\\\ \n",
      "  & (0.113) & (0.116) \\\\ \n",
      "  CUTTRANGE(15,20] & $-$2.029$^{***}$ & $-$1.995$^{***}$ \\\\ \n",
      "  & (0.299) & (0.323) \\\\ \n",
      "  CUTTRANGE(20, Inf] & $-$2.124$^{***}$ & $-$1.817$^{**}$ \\\\ \n",
      "  & (0.726) & (0.717) \\\\ \n",
      "  trange\\_norm\\_w7 & $-$0.029 & 0.003 \\\\ \n",
      "  & (0.145) & (0.153) \\\\ \n",
      "  CUTCLOUD\\_rean2(0,20] & 0.522 & 0.695 \\\\ \n",
      "  & (0.438) & (0.461) \\\\ \n",
      "  CUTCLOUD\\_rean2(20,40] & 0.382 & 0.510 \\\\ \n",
      "  & (0.461) & (0.490) \\\\ \n",
      "  CUTCLOUD\\_rean2(40,60] & 0.658 & 0.782 \\\\ \n",
      "  & (0.453) & (0.480) \\\\ \n",
      "  CUTCLOUD\\_rean2(60,80] & 1.001$^{**}$ & 1.135$^{**}$ \\\\ \n",
      "  & (0.482) & (0.518) \\\\ \n",
      "  CUTCLOUD\\_rean2(80,100] & 1.624$^{***}$ & 1.758$^{***}$ \\\\ \n",
      "  & (0.458) & (0.483) \\\\ \n",
      "  cloud\\_norm\\_7\\_rean2 & $-$0.030 & $-$0.036 \\\\ \n",
      "  & (0.023) & (0.025) \\\\ \n",
      "  CUTRHUM\\_rean2(0,20] & $-$4.676$^{***}$ & $-$3.776$^{**}$ \\\\ \n",
      "  & (1.250) & (1.780) \\\\ \n",
      "  CUTRHUM\\_rean2(20,40] & $-$1.480$^{**}$ & $-$0.995 \\\\ \n",
      "  & (0.625) & (0.634) \\\\ \n",
      "  CUTRHUM\\_rean2(40,60] & 0.009 & $-$0.021 \\\\ \n",
      "  & (0.191) & (0.195) \\\\ \n",
      "  CUTRHUM\\_rean2(80,100] & $-$0.328$^{**}$ & $-$0.340$^{**}$ \\\\ \n",
      "  & (0.136) & (0.139) \\\\ \n",
      "  rhum\\_norm\\_7\\_rean2 & 0.101$^{**}$ & 0.115$^{***}$ \\\\ \n",
      "  & (0.039) & (0.042) \\\\ \n",
      "  CUTWIND\\_rean2(5,10] & 0.484$^{***}$ & 0.439$^{***}$ \\\\ \n",
      "  & (0.118) & (0.129) \\\\ \n",
      "  CUTWIND\\_rean2(10,Inf] & 0.779$^{***}$ & 0.821$^{***}$ \\\\ \n",
      "  & (0.230) & (0.239) \\\\ \n",
      "  wind\\_norm\\_7\\_rean2 & $-$0.324$^{*}$ & $-$0.317 \\\\ \n",
      "  & (0.194) & (0.199) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes & Yes \\\\ \n",
      "Date FE & Yes & Yes \\\\ \n",
      "Admin_2:Month FE & Yes & Yes \\\\ \n",
      "Observations & 4,405,171 & 4,116,044 \\\\ \n",
      "R$^{2}$ & 0.284 & 0.286 \\\\ \n",
      "Adjusted R$^{2}$ & 0.274 & 0.277 \\\\ \n",
      "Residual Std. Error & 77.707 & 77.501 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{2}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{2}{r}{Standard errors are in parentheses and are clustered on the Admin 1 level} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# #Tables\n",
    "# invisible(stargazer(flex_t_binned_GHCND, flex_t_binned_GHCND_HIC,\n",
    "#           type='latex',\n",
    "#           title = \"X. Flexible Spec With/Without Data from Lower and Upper Middle Income Countries\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Flexible TMIN All Countries','Flexible TMIN Only High Income'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: Sleep Duration (Minutes)',\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\",\"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Admin_2:Month FE\", \"Yes\", \"Yes\",\"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the Admin 1 level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "   felm(formula = Awake ~ tmin + tmin_norm_w7 + prcp + prcp_norm_w7 +      trange + trange_norm_w7 + cloud_rean2 + cloud_norm_7_rean2 +      rhum_rean2 + rhum_norm_7_rean2 + wind_rean2 + wind_norm_7_rean2 |      userID + unique_day_no + stateyrmn | 0 | state, data = Climate_Controls_DF,      na.action = na.omit) \n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-1.1322 -0.2859 -0.1533  0.3816  1.2651 \n",
       "\n",
       "Coefficients:\n",
       "                     Estimate Cluster s.e. t value Pr(>|t|)    \n",
       "tmin                1.952e-05    9.860e-05   0.198 0.843056    \n",
       "tmin_norm_w7       -3.206e-04    4.006e-04  -0.800 0.423499    \n",
       "prcp                1.616e-03    4.821e-04   3.352 0.000802 ***\n",
       "prcp_norm_w7        1.145e-02    4.711e-03   2.431 0.015050 *  \n",
       "trange             -2.526e-05    9.129e-05  -0.277 0.781976    \n",
       "trange_norm_w7     -8.603e-04    6.565e-04  -1.310 0.190049    \n",
       "cloud_rean2         2.342e-05    1.045e-05   2.240 0.025060 *  \n",
       "cloud_norm_7_rean2 -3.162e-04    1.662e-04  -1.902 0.057108 .  \n",
       "rhum_rean2          3.681e-05    3.128e-05   1.177 0.239210    \n",
       "rhum_norm_7_rean2   4.092e-04    1.631e-04   2.510 0.012090 *  \n",
       "wind_rean2          3.805e-04    1.122e-04   3.390 0.000698 ***\n",
       "wind_norm_7_rean2   5.225e-03    2.655e-03   1.968 0.049068 *  \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 0.4252 on 4345352 degrees of freedom\n",
       "  (3005800 observations deleted due to missingness)\n",
       "Multiple R-squared(full model): 0.171   Adjusted R-squared: 0.1596 \n",
       "Multiple R-squared(proj model): 4.119e-05   Adjusted R-squared: -0.01372 \n",
       "F-statistic(full model, *iid*):14.99 on 59818 and 4345352 DF, p-value: < 2.2e-16 \n",
       "F-statistic(proj model): 10.23 on 12 and 1074 DF, p-value: < 2.2e-16 \n",
       "*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #AWAKENINGS\n",
    "# linear_tawakening <- felm(formula = Awake ~ tmin + tmin_norm_w7 + \n",
    "#                                           prcp + prcp_norm_w7 + \n",
    "#                                           trange + trange_norm_w7 + \n",
    "#                                           cloud_rean2 + cloud_norm_7_rean2 + \n",
    "#                                           rhum_rean2 + rhum_norm_7_rean2 + \n",
    "#                                           wind_rean2 + wind_norm_7_rean2 \n",
    "#                 | userID + unique_day_no + stateyrmn \n",
    "#                 | 0 \n",
    "#                 | state,\n",
    "#                 data = Climate_Controls_DF, na.action = na.omit) \n",
    "\n",
    "# summary(linear_tawakening)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}[!htbp] \\centering \n",
      "  \\caption{Nighttime Awakenings Linear Probability Model} \n",
      "  \\label{} \n",
      "\\footnotesize \n",
      "\\begin{tabular}{@{\\extracolsep{0pt}}lc} \n",
      "\\\\[-1.8ex]\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      " & \\multicolumn{1}{c}{Dependent Variable: awakening during sleep (change in prob >0 awakenings)} \\\\ \n",
      "\\cline{2-2} \n",
      " & Linear TMIN (GHCND) \\\\ \n",
      "\\hline \\\\[-1.8ex] \n",
      " tmin & 0.00002 \\\\ \n",
      "  & (0.0001) \\\\ \n",
      "  tmin\\_norm\\_w7 & $-$0.0003 \\\\ \n",
      "  & (0.0004) \\\\ \n",
      "  prcp & 0.002$^{***}$ \\\\ \n",
      "  & (0.0005) \\\\ \n",
      "  prcp\\_norm\\_w7 & 0.011$^{**}$ \\\\ \n",
      "  & (0.005) \\\\ \n",
      "  trange & $-$0.00003 \\\\ \n",
      "  & (0.0001) \\\\ \n",
      "  trange\\_norm\\_w7 & $-$0.001 \\\\ \n",
      "  & (0.001) \\\\ \n",
      "  cloud\\_rean2 & 0.00002$^{**}$ \\\\ \n",
      "  & (0.00001) \\\\ \n",
      "  cloud\\_norm\\_7\\_rean2 & $-$0.0003$^{*}$ \\\\ \n",
      "  & (0.0002) \\\\ \n",
      "  rhum\\_rean2 & 0.00004 \\\\ \n",
      "  & (0.00003) \\\\ \n",
      "  rhum\\_norm\\_7\\_rean2 & 0.0004$^{**}$ \\\\ \n",
      "  & (0.0002) \\\\ \n",
      "  wind\\_rean2 & 0.0004$^{***}$ \\\\ \n",
      "  & (0.0001) \\\\ \n",
      "  wind\\_norm\\_7\\_rean2 & 0.005$^{**}$ \\\\ \n",
      "  & (0.003) \\\\ \n",
      " \\hline \\\\[-1.8ex] \n",
      "User FE & Yes \\\\ \n",
      "Date FE & Yes \\\\ \n",
      "Admin_2:Month FE & Yes \\\\ \n",
      "Observations & 4,405,171 \\\\ \n",
      "R$^{2}$ & 0.171 \\\\ \n",
      "Adjusted R$^{2}$ & 0.160 \\\\ \n",
      "Residual Std. Error & 0.425 \\\\ \n",
      "\\hline \n",
      "\\hline \\\\[-1.8ex] \n",
      "\\textit{Note:}  & \\multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\ \n",
      " & \\multicolumn{1}{r}{Standard errors are in parentheses and are clustered on the Admin 1 level} \\\\ \n",
      "\\end{tabular} \n",
      "\\end{table} \n"
     ]
    }
   ],
   "source": [
    "# #Tables\n",
    "# invisible(stargazer(linear_tawakening,\n",
    "#           type='latex',\n",
    "#           title = \"Nighttime Sleep Interruptions Linear Probability Model\",\n",
    "#           model.numbers = TRUE,\n",
    "#           column.labels = c('Linear TMIN (GHCND)'),\n",
    "#           dep.var.labels.include = FALSE,\n",
    "#           dep.var.caption = 'Dependent Variable: awakening during sleep (change in prob >0 awakenings)',\n",
    "#           add.lines = list(c(\"User FE\", \"Yes\", \"Yes\",\"Yes\"),\n",
    "#                            c(\"Date FE\", \"Yes\", \"Yes\", \"Yes\"),\n",
    "#                            c(\"Admin_2:Month FE\", \"Yes\", \"Yes\",\"Yes\")),\n",
    "#           notes = c('Standard errors are in parentheses and are clustered on the Admin 1 level'),\n",
    "#           df=FALSE,\n",
    "#           header=FALSE,\n",
    "#           digits=3,\n",
    "#           no.space=T,\n",
    "#           font.size=\"footnotesize\",\n",
    "#           column.sep.width=\"0pt\"\n",
    "#           ))"
   ]
  }
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